conclusion writing generator

Studying / Writing Tools

Essay Conclusion Generator

Stuck at the end of your paper and not sure where to turn? We know it’s always good to go out on a high note and leave your reader wanting more. But what if you’re not sure how to do that? Well, don’t fret—our conclusion generator is here to help you hit that note over and over again. We take the words you’ve already used, the points you’ve already made, and the title that ties it all together to understand what you’ve been saying in your paper. Then we process all that data and turn out a perfect concluding essay for you. We’re making writing easy again!

How to Use:

This is a simple tool to use and all it requires is your title, your text, and a click of a button. First, enter the title of your paper into the appropriate box. This helps the generator get an idea of what your paper is about. Second, enter the text of your essay into the box below. The generator scans the text to find the thesis and main points. Then it uses that information to develop a concluding paragraph for you.

Conclusion Generator Results

Why use an essay conclusion generator.

Writing is an art—that’s all there is to it. Now we’re not talking about the Jackson Pollack kind of art where you get to splatter a bunch of colors across a canvas and then promote it as modern and meaningful—because to get noticed for that kind of art you have to know the right kind of people. Chances are you’re here because you don’t know the right kind of people. But, come to think of it, now that you’re here maybe you are finally meeting the right kind. Why? Because we’re the kind of people who want to help. We’re not going to tell your Pollack-like painted words are beautiful and throw money at you. No. But we will help craft that essay you’re struggling with. That’s what we do.

This conclusion generator is just one of many tools we offer, but it helps in a unique way that relates specifically to the art of making a great paper. How so? You see, every essay should have a beginning, a middle and an end—just like every great drama (as Aristotle used to say, you know). Sophocles’ Oedipus was considered the greatest example of a tragic drama by the Philosopher because, for one thing, it had a beginning, middle and end. Can you imagine what Aristotle would have thought of it if the play had cut off early, leaving the audience hanging on the edge of a cliff?

Well, he would have felt about the same way your reader feels if you write a big, beautiful essay with a beginning, middle but no end. Granted, in drama there’s a little payoff called catharsis—but in essay writing the payoff is essentially the final message: the Jerry Springer moment where he leaves you with a final thought, a parting few words to think about on your way home. That’s why writing a conclusion is so important. It is more than just rehashing your introduction and restating your thesis. It is about giving your reader that final scoop of ice cream—the one you’ve been holding back. He wasn’t even sure you had it but then, all of a sudden there it is! That’s what a great conclusion can be like.

So of course that brings the pressure, doesn’t it? You got your thesis. You got your intro. You wrote the body and gave every paragraph a main point. You finished that then the old mind went blank. Happens to the best of us. You’ve written your paper—and now what? You hit all the points you wanted to make and the last thing you want to do is go back through them all again. You’re exhausted. You’re out of gas.

What we did to design our generator was to think of what a great conclusion needs. A great conclusion should remind the reader in short summation of the main points of your essay. Your reader is about to go out the door, so you have to make sure he goes out with the right thoughts in his head. Don’t just repeat verbatim what you stated in your opening paragraph. Hit those points with a new set of words so that they seem both fresh and familiar at the same time. That way they stay embedded in the brain and the reader finds himself reflecting on them over time. Like a movie that you can’t get out of your head, an essay that concludes well can make up for all of its earlier sins and transgressions.

That’s why this generator helps. It gets you where you need to be and shows you what you need to do to wrap it up all nice and pretty with a bow on top. Think of your essay as a Christmas gift for someone you love. Are you really just going to hand it over unwrapped? That’s what you would be doing without a great conclusion. So use this generator and gift wrap that essay the way it should be. Your reader deserves it for reading all the way through after all.

Give Your Paper the Ending it Deserves

Whether you think of an essay conclusion as gift wrapping or as a wrap up, it makes no difference. The key to creating a great conclusion is to think about what your overall essay has been about and then write a set of new words inspired by that essence. The reader should feel that essence through and through. A quickly written conclusion that fails to tap into the essence will feel rushed and unsatisfactory. The reader will feel that after a great introduction and good meeting he got blown off at the end and not taken seriously. The reader wants to go out on a high not a low. So take a little extra time with your conclusion. Think of it as the last time you will see your reader, the last time you will get to say goodbye. Think of everything you’ve been through together in terms of your essay and then give the reader your final thought.

Our conclusion generator can help to find that final thought. If you’re brain is parched and thirsting for assistance, look no further because we’ve got the thirst quencher for you. This generator takes the text you’ve written, looks it over, then tells you want conclusion it should have. It digests the data and distils its essence and presents it for the reader like a new pearl on burnished silver. That’s what every great writer tries to do with his conclusion. Every essay needs one and every reader deserves one. Otherwise you’re basically sending him out into the cold without a final drink to keep him warm on the ride home, without a final thought to give him something to think about as he goes to sleep, without a final summation of all the things that matter.

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Conclusion Generator Online For All Types Of Papers

Minimum 200 words required

Psst... let's improve your conclusion

• Main ideas summarized
• Plagiarism Free
• No grammar mistakes

How to use conclusions generator:

• Everything you need is to insert your text and it's title into the box
• Click the button and get your conclusion done
• Enjoy the unique final paragraph!
• Would you like to get someone to write your summary and be confident that no points are missing? Ask our experts to write it for you!

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Conclusion Generator Review

Unlike other conclusion generator services, the Papersowl conclusion tool offers a user-friendly interface for plagiarism-free conclusions and anonymity.

How to avoid plagiarism?

Proper citation style.

Avoid plagiarism by always listing the source and formatting it correctly when you are note-taking. Take care of the proper formatting and citation style when using content from outside sources.

Write on your own

Avoid borrowing and overusing large pieces of the content from outside sources, especially from Wikipedia. Write your own thoughts and use sources only to support your opinion (remember to cite it though!).

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Conclusion Generator Online

Do you always find it difficult how to finish your paper and make all conclusions right? There is one beautiful thing like an online conclusion generator that would help you to put through your written and beautiful work neatly and nicely. The whole team of specialists took into account all the shortcomings. Many people from all over the world have already tried him and were pleased because regardless of the complexity of the text and its type, the conclusion generator will be useful to you.

Why It's Necessary To Use Conclusion Generator Tool?

It is very important to conclude because those words would be for a long time ahead of the reader and you should try it because you may not able to take into account all trivia and summarize them. If you think you are a master of inference at least just make an attempt to compare your one with computers and that it would be clear very soon.  But machines are evolving so rapidly that they really do more efficiently than people. When doing your homework take advantage of the conclusion generator for essay or research paper to be the best pupil in your class.

There are some websites where this service is paid and does not justify expectations. However, we provide users with absolutely free service because we care about our customers and want them to develop professional writing skills. And if you are used to writing your summarization by professional you may try as well order conclusion paragraph for an essay .

How Does Conclusion Generator Summarize Your Paper?

You might be thinking of what a mystic or I don't believe that is true but there is nothing difficult. Thanks to the easy calculations, the system auto defines an ideal variant for the outcome of the whole text. Here is how the conclusion maker works:

• Copy all important information from your paragraphs to make an inference
• Paste it into an appropriate place
• Press on summarize and wait a little for the best final part.
• And already very quickly you will get what you have been waiting for and you are pleased. Well, then you just need to copy this conclusion where you need it and that's all - the work is done.  

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Check these points to make your conclusion paragraph perfect.

Your summary won't be successful until your paragraphs and phrases are in good condition. Each of the paragraphs should reveal the research of the task, in order for the conclusion paragraph generator to weigh everything for and against for a wonderful illation.  But in no case should any of the paragraphs be in contradiction with the general idea that you reveal. I think that following all of the above rules, your inference deserves high recognition.

Also, do not deviate from the main idea of the text, because that makes your paper uninteresting to read and it looks as if you just filled a blank space. Keep in mind that you should avoid plagiarism or strictly adhere to the established level. This is no longer important for the free conclusion generator, but it is imperative for the job to be accepted. If you want to check the percentage of plagiarism, you can try plagiarism checker for your essay .

Best Professional Writers To Help You Finish Your Papers!

If you do not trust such artificial intelligence as the free essay conclusion generator, seek help from specialists. Our writers are highly qualified in the field of writing. They are ready to do any part of your work, as well as summarize the text. The main advantage and pride of our site are that we provide the opportunity for users to choose their own writer. Moreover, you are able to see his profile, the number of as well as comments on his work and rating. Here you can apply for help at any time of the day, and even if you have an urgent task, it can quickly be fulfilled by arrangement.

That's not all. We also provide users with the opportunity to communicate online with writers free of payment for greater convenience and benefit. Explaining the task the paper will be performed, even if the topic is extremely difficult and you have already been rejected from other websites. If you do not satisfied with some points, then this work either is done once again or given back. If you need our workers also can write your essay .  

To finish your paper, you may always use the conclusion generator, which would be a great thing in your hard work writing. This is a unique opportunity to have an online assistant at your fingertips anytime. Also, with any difficulty, you may apply for help to our site – we are appreciated by many of our regular users and you can easily join them. You just have to decide, and we are always open to our customers.

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How Does Conclusion Generator Work??

• Enter a title and a minimum of 200 words from your paper in the appropriate boxes.
• Click on generate button to get unique conclusion.
• You can paraphrase it, or copy the result into your paper.
• Optionally, you can improve your conclusion by ordering custom help from our writing experts.
• Reliable Editors
• Any Field of Study
• Fair Prices

Free Conclusion Generator is rated 4.9 /5 based on 701 user reviews.

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Advantages of Conclusion Generator By PapersOwl

Having trouble writing a powerful conclusion for your papers? Use our generator, which offers many advantages.

While competitors charge money for such services, we offer our Conclusion Generator free!

We guarantee the original papers, written by our authors exclusively for you.

We care about the quality of our programs, so our algorithms can always generate qualitative results

You don't have to wait long for the result. After you have entered the piece of your paper, the system will generate an output within a few seconds.

Many students have already confirmed that our free tool is a great and convenient feature that helped them detect and fix errors that could lead to a failure. With us, you will no longer need to look for a different scanner!

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Conclusion Generator for Your Essay

Number of sentences in results:

Looking for an essay conclusion generator? Try our tool! It will summarize your text and generate a conclusion paragraph in seconds. Get your beautiful ending here!

A key to a lasting impression is a dynamic and memorable concluding paragraph. The way you finish your text plays a crucial role. Through conclusion, you can make the readers think of a particular issue, engage them in further investigation of the topic, or even motivate them for action. Our automatic tool will help you end your essay effectively.

How do you use our conclusion generator?

Follow three simple steps:

• Insert the text you need to summarize in the box.
• Click the button.
• Enjoy the result!

Doubting whether our tool is worth using? Continue reading to see that our conclusion maker is a perfect option for you. In the article prepared by our team , find some useful tips on how to write a conclusion for an essay, research paper, etc.

• ️🔮 Why Use It

📎 Linking Words

✒️ restated thesis, 🖇️ summary & connections, 📌 final words.

• ️🚫 Conclusion to Avoid
• ️❓ Conclusion Paragraph Generator FAQ
• ️📍 References

🔮 Essay Conclusion Generator: Why Using It?

Essential advantages of this conclusion maker are as follows:

In other words:

• It works with different paper types. No matter what you are writing (an essay or research paper ), our tool will generate an appropriate concluding paragraph for you. Our machine can handle any type of text!
• The conclusion generator is online and user-friendly. You don’t have to download any apps or install special software. Everything is simple. You go online, insert the text, click the button, and get your conclusion!
• No registration or payment is required. Our conclusion maker is absolutely free. What’s more, you won’t spend any time on registration. You can use the tool right away.
• You’ll get your conclusion in a second. The tool is fully automatic and generates the summarized text within a moment. So, you will save a lot of time.
• We guarantee total privacy. The texts you insert and the concluding paragraph you get won’t be saved in the system. Don’t worry about confidentiality and plagiarism issues. Your privacy is our top priority!
• You can use it multiple times. Use our conclusion generator as much as you need! We don’t establish and limits or free trials.

🔎 Generating a Conclusion: 4 Components

Our generator will make a significant part of the work. Yet, we recommend you polish the result since any automatic tool may make certain inaccuracies.

Let’s start with the basics: what are the purposes of a conclusion?

• It leaves the final impression on your reader.
• It wraps up your piece of writing.
• It proves to the reader that you accomplished your goal.

To ensure the flawlessness of your concluding paragraph, you should have a clear understanding of how it should look like. In the following sections, we will discuss the essential elements of a strong conclusion.

To make the transition to the final paragraph smooth and logical, use linking words. Sometimes, it might be challenging to choose the most appropriate one. Here, we will tell distinguish between effective and weak linking words.

A writer aims to deliver information clearly and logically. The words that help you connect your ideas within and between the paragraphs are called linking words (or transitions). They ensure the smooth flow of sentences and play a crucial role in making the text coherent.

Now, let’s figure out what transitions are indeed effective in academic writing! Make sure the conclusion paragraph generator chose the most appropriate word.

Once you’ve linked the main body of your essay with your concluding paragraph, you need to connect it with the introduction. Do it in the topic sentence, which is the first one in the paragraph. Here you should restate your thesis statement that you’ve previously written in your introduction.

Here’s how you restate a thesis in your conclusion:

Change the wording.

Use a wide variety of synonyms the English language offers you! Experiencing some troubles with finding appropriate alternatives? Use an online generator! But do it carefully: always check if the word fits within the context and doesn’t confuse the readers.

Use a different structure.

There are so many ways to do that. Use different tenses, grammar constructions, or just present your main points in a new order. These simple tricks will effectively differentiate your topic sentence from your thesis statement.

Separate your key points.

In the introduction, you’ve probably listed your main points in a row. However, as you investigated the topic, you can make your conclusion more complex and present your critical ideas from a broader perspective. Spread them across the entire paragraph and prove to your readers your competency.

For example, your thesis statement might be the following:

Living in a city is better than living in a village because cities offer more educational and career opportunities.

Now, let’s try to apply the three mentioned-above strategies and restate our thesis:

Life in a city is a perfect choice for a modern highly-motivated person. Here, any individual will have the possibility to get an education in a preferred field. Moreover, urban dwellers can actualize themselves throughout their fascinating career path.

In the last paragraph, you need to retell what the text was about. Our conclusion writer is a perfect tool to complete this task. But there are still a couple of things you should be aware of:

• Don’t just summarize but synthesize: connect the arguments and logically.
• Don’t provide any new supporting details in the last paragraph of your paper.
• Don’t add any new points, ideas, arguments in your conclusion.

And bear in mind that your concluding paragraph should include:

• Key ideas. Identify the essential points and restate them in your conclusion. Avoid including any secondary information – only the most crucial ideas.
• Ground facts. Remain objective. Include the facts you based your key ideas on in your conclusion.
• The connection between ideas and facts. Demonstrate a clear correlation between your main points. Convey that even the supporting facts are linked in some way. It will prove to the reader your credibility and professionalism in the chosen area.

Use your concluding sentence to make the last good impression on the reader. To achieve it, you can restate the start of your introduction, provide a rhetorical question or call-to-action. Let’s explore some tactics for making the closing words memorable.

Overall, your last sentence should:

• provide a sense of closure;
• demonstrate the significance of your findings;
• leave a long-lasting impression;
• motivate a reader for action, if necessary;
• wrap up your essay on a positive note.

Does it seem to you that the last sentence created by the essay conclusion generator is too simple? Wondering how to conclude your piece of writing dynamically? Consider applying one of the following strategies to improve the text generated by the automatic tool:

• Framing. Make the first and the last sentences of the paper identical/similar/complementary.
• Call to action. Motivate your readers to perform the activities that would change their lives, bring value to society, etc.
• A joke. Include a short anecdote at the end of your essay to leave a positive vibe.
• A quote. Sometimes, an impressive saying can make your reader remember your paper for a long time.
• A question. Leave the food for thought for your readers so that they will be willing to explore your topic further.

🚫 Conclusion Types to Avoid

Now you know what should be in your conclusion. It’s time to discuss what shouldn’t be there!

Four strategies for wrapping up the text you should avoid:

Raw thesis restatement.

Indeed, you should refer to your thesis statement in your conclusion. However, it doesn’t mean that you can just paraphrase it. The ending of your paper will be too short and weak. Instead, you should develop your thesis statement, adding the findings you’ve got while writing the text. It will show that you did learn and achieve something, composing the paper.

Revealing effect.

Sometimes, students wrongly assume keeping the thesis secret until the very conclusion is a powerful strategy to make the readers intrigued. Well, it may work out in fiction literature. But for academic essay writing, this is not a beneficial tactic. Here, you need to come up with a clear thesis statement in the introduction. Then, structure your arguments according to it. In your conclusion, you should restate your thesis, not mention it for the first time.

Emotionless essay conclusion phrases.

Something like “I love my mother very much” will not impress the reader. Try to be more creative and emotionally appealing. How about ending your piece of writing in this way: Charley Benetto has once said: “When you are looking at your mother, you are looking at the purest love you will ever know.” I believe this is the best description of the feelings that awake inside my heart when I’m close to my mom.

Too broad conclusion.

The best way to end an essay is to create an impressive and concise concluding paragraph. Do not include any unnecessary information, irrelevant facts, or random arguments here. By the way, we know how to prevent this mistake. Use our online conclusion maker and be sure your last paragraph includes only indeed essential ideas.

Thanks for reading this article. We hope our automatic conclusion writer can help you complete any written work correctly. Share it with your peers who may need the tool as well.

❓ Conclusion Paragraph Generator FAQ

❓ how to make a conclusion for an informative essay.

An informative essay aims to provide information on a given topic. These texts are usually not long. That's why your conclusion should be short. Take the topic sentences from the body paragraphs of your informative essay and restate them. Add your personal opinion neither to the essay body nor to its conclusion.

❓ How to write a conclusion for a compare and contrast essay?

In a comparison essay, you compare and contrast two or more objects. To conclude the paper properly, you'll need to restate your thesis and briefly summarize the results of the comparison you've made. Adding some final insights and your impressions is also a good idea.

❓ How to make a conclusion in a persuasive essay?

A persuasive essay aims to convince its readers to accept a particular point of view. That is why you should add a call to action to the summary and the restated thesis, which are the standard components of a conclusion. Remember: the last statement of your paper should impress your audience.

❓ What is a conclusion tool?

The conclusion paragraph generator on this page is an online tool that can help you summarize your essay into a short and sweet conclusion in a couple of clicks. All you need to do is insert the text, click the button, and enjoy the result.

📍 References

• Conclusions – The Writing Center, the University of North Carolina at Chapel Hill
• Ending the Essay Conclusions – Pat Bellanca, for the Writing Center at Harvard University
• Writing Effective Conclusions – Writer’s Web, Writing Center, the University of Richmond
• Conclusion – Academic Writing Help Centre (AWHC), Student Academic Success Service (SASS), University of Ottawa
• Essay Conclusions – UMGC, the University of Maryland, Global Campus
• Difference Between Summary and Conclusion (with Comparison Chart) – Surbhi S, Key Differences
• How to Write a Summary of an Article – Virginia Kearney, Owlcation Education

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Free Conclusion Generator to Finish Your Essay

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Let our writing experts finish this work for you

Anyone who ever wrote essays knows how annoying summing them up is. You’ve done all the work already, and you have no patience for the last part — it seems like a waste of time. Conclusion generator could become your most loyal assistant here. WritingUniverse made it easily accessible, prompt, and efficient. This tool could generate a closing section for your paper before you blink, and it’s going to be logical and on point with what you wrote. Sounds too good to be true? Learn the magic behind the machine and everything will become clear!

Common Reasons for Using Conclusion Paragraph Generator

Being a  trusted essay writing service  is a great honor, so we do our best to meet students’ needs in all possible ways. Creating an efficient conclusion generator is one of the first things we did, and here’s why. Every student has to write essays. For many, this is a boring and time-consuming job, but even those who enjoy doing it face a problem. By the end, the enthusiasm and interest fade. You cannot include any new information into conclusion, you just need to restate all major points from your previous paragraphs. Such a task is monotonous and irritating because you’re so close to being done with it — you can sense the freedom, if only this stupid paragraph went away. With automatic generator, it does. Our conclusion maker generates a text students need within several seconds. They won’t have to think deeply about which points are the most important or how to put them together, it does this kind of work for them. It relies on their text alone, without taking info from the Internet, meaning that they won’t have to worry about plagiarism. Do tools like this make mistakes? Unfortunately, yes. Machine is machine, and they might mess up badly. But through series of tests and improvements, we made sure to minimize negative outcomes and bring only the best results to you.

Benefits of Using Concluding Generator

When it comes to conclusion paragraph maker, its benefits seem obvious. It’ll craft a closing section for your essay, what else to add here? You’d be surprised! Have a look at these four ways in which conclusion builder could help you.

• Underlining paper’s essence. Students often search for  good research paper topics  and try to explore them in depth, but sometimes, they lose their point. They explore one aspect of a problem after another, jump between different ideas, and before they know it, their paper loses its direction. Concluding paragraph generator will pick only those parts that have the biggest relevance. By looking at a generated last section, you’ll remember what your goal was, and in the end, it will make your writing much stronger.
• Giving ideas for free. Our summarizer is completely free. Students don’t have to pay or even create an account. As they use it, even if they don’t like the first results, they’ll be able to see what points they could include to make their conclusion sound great. It’s a win-win: even flawed outcomes of conclusion tool could be inspiring and educative.
• Saving time. No need to waste an hour on finishing an essay when students could use conclusion creator. It’ll do everything in mere seconds, all they’d have to do afterward is some editing.
• Teaching the rules. By using generator, students see technical aspects of conclusion building. For example, they could take note of the size, which shouldn’t exceed 10% from the word count in total, lack of direct quotes, etc.

There are more ways to summarize your paper. WritingUniverse has many  essay examples free  of charge on our website. Read them and pay attention to conclusion in particular. Students could also hire a human expert who would do this work in a 100% efficient way. It’ll be cheap since this is just one part, but you’ll like results much more.

How Does Essay Conclusion Generator Work?

A lot of students want to understand how generators work before they trust them. We understand this, so we’d like to explain the basics. Our machine follows special algorithms: it searches through your text and selects vital sentences by analyzing number, frequency, and location of key words. It also sticks to the principles we instilled in it regarding the size and some other things. Using our essay conclusion maker is a piece of cake. Students should simply insert their essay into a box and click the button. In a few seconds, they’ll see their paragraph. They could copy it and use it as they want. The only problem is, some professors demand conclusions to be written from scratch. Taking your own sentences and reusing them, even if you mixed them up beforehand, won’t work here. In such cases, we recommend placing an order with a human expert. They could have your conclusion done by your deadline, and like we said, it won’t cost much.

Let Tools and Experts Help You and Enjoy Your Free Time

At WritingUniverse, students could find anything they want. If finishing your essay is bothering you, try our free generator and see how it sounds. If results don’t satisfy you, use services of our human conclusion writer. You could also rephrase all sentences our tool generated for you. There are endless ways of saving your precious hours and completing boring work in minutes: learn about them, pick the one that you like most, and go for it!

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What are the strategies for writing a good conclusion?

Don’t introduce new facts or quotes. Restate your thesis, include major points from each body paragraph, and tie them all together logically. Add recommendations for future research and/or admit limitations of your work if it seems fitting.

How should I summarize an article?

Other than using concluding paragraph maker, avoid adding personal thoughts or analysis. Present information in a cool, objective way even if you disagree with something. Choose the most interesting and relevant bits to summarize for your audience.

How to write a concluding sentence?

This is something that should come instinctively. Re-read your closing paragraph: what is the first line that comes to your mind? If you still cannot think of anything, try conclusion sentence generator — it’ll give you ideas.

What is a conclusion maker?

It is an automatic tool that analyzes a text, picks parts from it, and puts them together in one complete paragraph. The efficiency of such machines can be high, but you should still read what they create to make certain everything’s fine.

What is the best way to end an argumentative essay?

You need to repeat thesis and your position by using a strong voice. List the major pieces of evidence you came up with in the body to support your arguments; remind audience of how you arrived at your conclusion. Try conclusion generator for free and get some ideas.

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• Conclusion Generator

Conclusion generator lets you effortlessly create the final part of your article based on the provided content within seconds.

This conclusion paragraph generator analyzes the entire input text, selects the most relevant sentences, and combines them into a concluding paragraph.

How to write a conclusion for an essay?

You can use this free online tool to write a conclusion for an essay and any other content.

To generate an ending paragraph by using our tool, follow the below steps:

• Type or paste your content into the input box.
• Or, import files from the local storage.
• Click on the Generate button.
• Copy the output text in real-time and paste it wherever you want in real-time.

Why use our Conclusion Generator?

Sometimes, it is difficult for students to create a good ending at the end of an essay or research paper.

Our concluding paragraph generator makes it easy for them to create a quick ending paragraph for your work in less time.

Furthermore, you can use our conclusion maker because of its below best features:

No Registration

You don’t have to complete any registration process to compose ending paragraphs of essays, research work, or articles.

Simply go to Editpad.org , search for the Conclusion Generator, select the tool, and start composing the ending paragraph for any content effortlessly.

Free for everyone

You don’t have to buy any subscription plan to use this online utility. It is completely free and 100% secure to use for everyone.

Supports Different Files

Our conclusion sentence generator supports different file formats. Just click on the Upload File icon and import your files in TXT, DOC, and PDF formats.

Quick Results

This online tool comes with an easy-to-use interface and creates an effective concluding paragraph within a fraction of second.

No Limitations

Our free conclusion generator has no limitation on composing conclusions for essays and assignments. You can use this online conclusion maker as many times as you want without any hassle.

Copy text to clipboard

This feature helps you to copy the output conclusion at the same time.

Simply click on the Copy Icon and copy-paste your results in real-time with one click.

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Conclusion Maker for Essays

Copy and paste your text

Number of sentences in results:

The tool is relatively simple in use:

• Copy the text that needs a memoizable conclusion. It should not exceed 20,000 characters.
• Paste it into the respective field of conclusion paragraph maker.
• Press the “Summarize” button.
• Enjoy reading your free and accurate conclusion!
• 6 Tips for a Good Conclusion
• 4 Strategies to Avoid

🔗 References

❓ conclusion maker: questions & answers.

A conclusion in the final part of almost any written genre. It is a summary of the key ideas. This section describes the cornerstone of the author’s opinion.

A conclusion usually has distinctive starting words, like “To sum up” or “Having considered everything mentioned above.” They show the reader that the author has fully expressed their thoughts, and nothing new will be said. Still, A conclusion is an essential part of an essay because it draws the line under the writer’s reasoning.

A conclusion is the part of any writing the readers will remember the most.

Rephrase your thesis statement in a single sentence.

Then, outline the central ideas (in a descriptive essay) or arguments (in a persuasive or argumentative essay).

Finish your conclusion with a call for action or analysis of the prospects of the discussed issue.

Its length depends on the overall word count of your essay. For example, the conclusion of 100 words would suffice for a 4 -page paper. You can write your conclusion by yourself or use a Conclusion Maker.

A conclusion sentence can be necessary at the end of each main body paragraph and at the beginning of the conclusion paragraph.

In both cases, you can start with: in conclusion, all in all, therefore, as a result, lastly, thus, or finally.

But while the last sentence of the main body paragraph is a summary of the same passage, the first sentence of a conclusion is a restated thesis statement . To make it correctly, consult your introduction and write it in line with what you have mentioned in the beginning.

The answer is yes. Conclusion makers for essays do work . These online tools analyze what you have written and give you the final part to conclude with. A conclusion paragraph maker helps you avoid excessive wordiness, preserving the gist.

Since you can adjust the number of sentences you need in your conclusion, the Conclusion Maker can make it as condensed or as broad as required. Certainly, you can correct some places, if you wish so. But the overall quality of summaries produced by conclusion generators is high.

🤗 Writing a Conclusion: 6 Useful Tips & 4 Failing Strategies

A good conclusion is a cherry on top of your writing. It explains how your research or analysis could be helpful for the reader. It also provides an insight into further development of the same topic. This approach shows that you have done only some part of the work, and other researchers may build upon your experience.

Roughly speaking, any conclusion should consist of the following parts:

• Topical sentence. It can include your thesis statement expressed in different words or a general summarizing sentence (although this variant is usually weaker).
• List of the central ideas, one sentence for each.
• Call for action or suggestions for your successors in research.

The following precautions will help you make an accurate and comprehensive.

6 Tips for Writing a Really Good Conclusion

1 Ask your readers a provocative question.

Do you think a country could function without legislation? Could you imagine a society that speaks a dozen of languages? Would you drive your car today if you knew it would kill your child in 40 years? All these questions are provocative, i.e., they make your reader think. By the way, it is also a perfect tip for an introduction.

2 Propose a solution for the problem.

This tip usually works in research papers. After all, finding a solution is the purpose of most scientific work. But it can also be a successful strategy in an essay, especially if you have developed an untrivial approach to the issue.

3 Evoke an image of what will happen if the situation persists.

This strategy raises the topicality of the analyzed issue. The most significant problem of human perception is that we ignore small red flags until we get overwhelmed by their accumulation. Draw the picture of those red flags raised to the power. And then, leave without saying goodbye, allowing your readers to think.

4 Call your readers for action.

It is an alternative to points 2 and 3. If you have no ready-made solution to propose, and the possible outcome is evident, ask your readers to do what they should. For example, highlight the urgency of research in this sphere or call them to stop doing such or another thing.

5 It should be a synthesis, not a summary.

Roughly speaking, do the readers’ job for them. Guide them to the thoughts you wanted them to ponder. Make them think: “I should have guessed that!” It’s not an easy task, but with experience, you’ll become a master in it.

6 Perform the “So What” test.

This strategy works well with all the five mentioned above and any other possible conclusions. It is simple: read your closing paragraph and ask yourself, so what? Why should your audience care about the problem? If there is an answer, that’s it. If there is no, write it as the last sentence.

4 Failing Strategies to Avoid

One can find out what it means to write a good conclusion only by exploring the bad ones. The UNC at Chapel Hill creatively generalized the worst possible strategies to compose the final paragraph. The types are laughable, but how many times have you written something like that? Besides, our conclusion paragraph maker never generates such passages.

1 Captain Obvious.

This conclusion type limits itself to say, “That’s what I think. Thanks for your attention.” It is the most popular strategy among inexperienced essay writers. For your professor, it means that you do not realize the importance of your topic or how it can fit into a broader analysis.

Distinctive features:

• A patchwork of the thesis statement and the topical sentences
• No morale / generalization / call for action
• Dull and obvious

2 Sherlock Holmes.

Some students imagine themselves detective story writers. In an attempt to make their essay engaging, they resort to suspense elements. In other words, the reader finds out what it was all about only in the end. But while it is a good practice to reveal the killer’s identity in the last chapter, it is not so with regard to academic writing. Stick to the genre, as the reader expects no mysteries from a college essay.

Distinctive features (you will find out it is “ Sherlock Holmes ” while reading the main body):

• Vague till the end
• Resembles creative writing, not an academic paper
• No or little analysis
• The conclusion sets the cards on the table, explaining all the examples in the text

3 Drama Queen.

The UNC at Chapel Hill calls it “America the Beautiful,” ”I Am Woman,” or ”We Shall Overcome.” The authors of such conclusions appeal to the readers’ emotions at the expense of meaning, analysis, and rationalism. And although such feelings can be authentic and relatable, academic writing is not the right place for them.

• Off-key emotional pitch
• Useing powerful, heartfelt words (a hero, an immense contribution, a literary giant, etc.)
• The author’s emotions are more important than critical analysis
• Sounds pathetic

4 Grab Bag.

This conclusion type is the second most popular after “Captain Obvious.” Imagine a diligent student who has performed a thorough analysis of literature. They have found or invented more ideas on the topic than their essay length could house. And not to waste their intellectual work, they include some of those thoughts in the closing paragraph. What’s the problem? A conclusion is not made for that.

• The conclusion resembles another main body paragraph
• No feeling that the writing comes to its end
• A mixture of random facts and evidence
• Creates confusion

Hope the advice above was useful for you. Good luck with your writing! By the way, a good final step of your work on any text would be checking it for plagiarism. Don't hesitate to use our free plagiarism-detecting tool to do that.

Updated: Oct 25th, 2023

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Whenever you need to come up with a short summary or conclude your paper in a couple of sentences—visit this page. The tool available here will make this task possible to complete in a few clicks. Give it a try and you’ll find a lot more ways you can use it to improve your creative process.

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AI-powered Conclusion Generator

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A Conclusion Generator is a valuable tool that uses advanced AI technology to help you create compelling conclusions easily. This amazing tool analyzes your content, summarizes the main points, and generates a conclusion that leaves a lasting impression on your reader.

One such tool, standing out among others in terms of accuracy and extensive customization options, is Junia AI' s Conclusion Generator . It not only provides precise conclusions but also allows you to tailor the output to align with your specific writing style and tone.

Using a conclusion generator offers several key benefits across various writing scenarios:

• Effortless creation of effective and engaging conclusions
• Significant time-savings for writers
• Enhanced professionalism in documents by providing a strong finish

Whether you're a student striving for an impactful end to an essay or a professional writer looking to save time without compromising on quality, Junia AI's Conclusion Generator can be your efficient aid.

Understanding Conclusion Generators

Conclusion generators , as the name suggests, are AI-powered tools designed to provide compelling conclusions for various types of written content. These tools play a critical role in summarizing the key points of your content and leaving a lasting impression on the reader. The primary intent is to wrap up your arguments or discussions effectively, providing a sense of closure to your readers while emphasizing the importance of your message.

To understand how conclusion generators work, let's delve into their process:

• Inputting Main Content : You start by providing your main content or key points to the tool. This could be an essay, a report, a blog post, or any other form of written content.
• Synthesizing Information : The tool utilizes advanced algorithms to analyze and understand the context and essence of the provided content.
• Generating Conclusion : The tool then generates a concise and relevant conclusion that encapsulates the crux of your content.

The beauty of these advanced AI-powered tools lies in their ability to process large amounts of data swiftly and accurately. They can dissect complex narratives, extract meaningful insights, and present them in a condensed yet impactful conclusion.

Benefits of Using Conclusion Generators

Crafting effective conclusions with ease.

Students face the challenge of synthesizing complex ideas into a concise conclusion that reinforces their thesis and main arguments. With Conclusion Generator, students can overcome this hurdle, producing well-rounded conclusions for their essays and reports. Here's how:

• Instant Summarization : Students input key points or sections of their paper into the Conclusion Generator, which then synthesizes these inputs into a cohesive summary, reiterating essential arguments and findings.
• Alignment with Academic Standards : The tool ensures that the conclusion adheres to academic writing standards, making it suitable for essays, research papers, and theses.
• Customization : Users have the flexibility to refine the tone, ensuring that the conclusion matches the voice of their essay or report.

Consider a student working on an essay about climate change. After hours spent developing arguments and analyzing data, they might struggle with summarizing their thoughts powerfully. By using a Conclusion Generator, they input their paper's main arguments—perhaps the impact of greenhouse gases on global temperatures and policy recommendations. The tool then generates a conclusion that encapsulates these points while providing a strong closing argument for immediate action against climate change.

The benefits are clear:

• Students enhance their writing with conclusions that pack a punch.
• They save valuable time during the writing process.
• The final product is polished and professionally presented.

By integrating Conclusion Generator into their workflow, students ensure no critical point is overlooked in their conclusion. This allows them to present a compelling final section that resonates with readers and effectively encapsulates their research's significance.

With examples like these, it is evident that students gain an edge by leveraging AI tools like Conclusion Generator to elevate their academic writing. This advantage translates into improved grades, increased confidence in writing abilities, and mastery over one of the most challenging aspects of essay composition—the powerful conclusion.

Saving Time for Writers

Writers have a tough job. They need to create interesting content and meet deadlines. To do this, they must work efficiently while still producing high-quality work. This is where Conclusion Generators come in handy.

Conclusion Generators, like Junia AI's Conclusion Generator, help writers be more efficient . They do this by generating short yet powerful endings for articles or essays. This saves writers time that they can use for research or writing other parts of their piece.

The great thing about these tools is that they can take a large amount of information and condense it into a concise conclusion. This is especially helpful for professional bloggers who have to write multiple articles in a day. With a conclusion generator, they can quickly wrap up their posts without worrying about how to end it.

"As a writer constantly racing against deadlines, I find Conclusion Generators to be invaluable. They help me conclude my work promptly and effectively." - John Doe, Novelist and Freelance Writer

Conclusion Generators are not just useful for creating endings; they also boost the productivity of writers by saving them time.

Enhancing Professional Documents

Professionals in different industries know how important it is to communicate clearly and effectively. That's where a Conclusion Generator can make a big difference. When creating business materials like reports, presentations, or client proposals, the way you wrap things up can have a strong impact on how readers perceive and decide things.

• Business Reports : A Conclusion Generator takes key data and insights, and turns them into a concise summary that highlights the main findings and their significance for stakeholders.
• Presentations : If you want your audience to remember your main message and take action, a Conclusion Generator can help craft a powerful closing statement.
• Client Proposals : With a Conclusion Generator, professionals can make sure their final pitch is compelling and effectively captures the unique value of their proposal.

By using a Conclusion Generator as part of their work process, professionals can save time while improving the quality of their writing. The tool helps to tie up loose ends and provides readers with a clear understanding of what comes next or the main point being made.

Adaptability Across Writing Formats

One of the great things about these AI tools is their ability to work well with different types of professional writing:

• They can adjust their style to match the level of formality required.
• They understand the specific language used in various industries.

This flexibility makes them extremely valuable for professionals who strive for excellence in all aspects of their written communication.

Consistent Quality with Advanced Algorithms

With the precision offered by advanced algorithms, professionals can maintain consistent quality in their conclusions, which often leads to better business results:

• Summarizing complex financial analyses
• Concluding strategic recommendations

In both cases, having a reliable tool like a Conclusion Generator can make the process easier and more effective.

Overall, the Conclusion Generator by Junia AI stands apart with its advanced AI capabilities, customization options, and versatility in adapting to various writing styles. With this powerful tool at your fingertips, you can easily craft well-rounded conclusions for academic papers, save time on article writing, and enhance professional documents.

Frequently asked questions

• What is a Conclusion Generator? A Conclusion Generator is a valuable tool that uses advanced AI technology to automatically generate conclusions for various types of written content.
• How do Conclusion Generators work? Conclusion generators, as the name suggests, are AI-powered tools that analyze the content and context of a piece of writing to generate relevant and effective conclusions.
• What are the benefits of using Conclusion Generators? Using conclusion generators can help writers craft effective conclusions with ease, save time, enhance the quality of professional documents, and adapt to various writing formats.
• How can Conclusion Generators help students? Students face the challenge of synthesizing complex ideas in their academic work, and conclusion generators can assist them in crafting effective conclusions with ease.
• How do Conclusion Generators save time for writers? Writers have a tough job. They need to create interesting content while meeting deadlines. Conclusion generators can help them save time by automatically generating conclusions for their written work.

Essay Conclusion Generator

Ai-powered essay conclusion tool.

• Finish a school essay: Create a compelling conclusion that summarizes your arguments and restates your thesis.
• Conclude a research paper: Generate a succinct conclusion that wraps up your findings and leaves a lasting impression.
• Close a persuasive essay: Craft a powerful conclusion that reinforces your arguments and persuades your readers.
• Conclude a narrative essay: Create a memorable conclusion that ties together your story and leaves your readers satisfied.
• Finish a college application essay: Generate a compelling conclusion that leaves a lasting impression on the admissions committee.

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Introducing Originality.ai's Conclusion Generator

So you've pushed through blank page syndrome, gotten into the zone, and written up stellar content that fits your vision. your writing starts strong, hits all the right points, and flows nicely. but you're stuck right before the finish line, unsure of how to tie things together well and end with a strong conclusion..

So you've pushed through blank page syndrome, gotten into the zone, and written up stellar content that fits your vision. Your writing starts strong, hits all the right points, and flows nicely. But you're stuck right before the finish line, unsure of how to tie things together well and end with a strong conclusion. Been here before? Originality.ai makes this pattern a thing of the past with our new free conclusion generator. Once you've done the hard work of producing content you're proud of, Originality.ai helps you carry it to completion and finish strong without spending precious time stuck behind writers block, all while maintaining your unique voice. 

Armed with the power of advanced AI, our easy-to-use conclusion generator analyzes your writing, identifies the key ideas and central arguments of your work, and produces a conclusion that matches the tone and style of your input. Perfect for essays, articles, or blog posts, our conclusion generator helps you to whip up closing paragraphs at lightning speed , streamlining your workflow and making your writing stronger than ever.

Finishing Strong: Use Cases for a Conclusion Generator

Are you constantly stuck trying to wrap up your writing with a compelling conclusion? Orinality.ai's new conclusion generator tool is your new best friend when it comes to tying up all those loose ends in your writing. Whether you're a student struggling to conclude an essay, a busy content creator trying to keep your blog posts punchy, or a professional crafting reports and presentations, this tool has got your back. One of the best applications of this tool is in the realm of business communication. Imagine you're crafting a report or a proposal, where it's vital to conclude with a confidence that really drives your points home. This is where our AI conclusion generator becomes your secret weapon. Navigating through complex data, market analysis, or strategic plans to arrive at the perfect conclusion can be challenging. However, with this tool at your disposal, all you need to do is feed in the key elements of your discussion, and voilà – it generates a clear, effective conclusion that captures the essence of your work, at the level of detail that's right for you.

But wait, there's more! Our free conclusion generator is more than just a time-saver; it's a quality enhancer. For bloggers and content creators, crafting a memorable ending that resonates with your audience is crucial. This tool helps you do just that, by providing a tailored conclusion that aligns with your writing style and the message you want to convey. As the University of Southern California points out , the conclusion is the opportunity answer the question “So what?” What is it about your work that matters most? Imagine ending every post with the perfect punchline or thought-provoking statement – that's what this tool offers. Plus, it's incredibly user-friendly and adaptable, making it suitable for a wide range of writing styles and purposes. In essence, this AI tool isn't just about ending your writing; it's about giving it a powerful, lasting impact.

What's included?

Originality.ai's conclusion generator's strength is its flexibility. Need your conclusion that fits your writing ASAP? Simply type, paste, or upload the text you want to conclude, pick your desired length, and click generate. Presto – our sophisticated AI tool identifies the core details of your writing and serves up the perfect conclusion. Need a little more control over your conclusion, to make sure it hits just the right tone and detail level for your audience? Just open up the settings option to tailor your conclusion to your needs.

Diverse language options allow you to conclude from, or in, a variety of languages, helping you reach any audience, and our structure settings allow you to format your conclusion as either a paragraph, a numbered list, or a set of bullet points, making it as useful for presentations and slide decks as it is for more traditional writing.

No matter which options you select, our conclusion generator consistently delivers content that is well-written, easily understandable, and grammatically flawless. This guarantees that you always present your most polished work, helping you reach new heights with your writing.

Step-by-Step Guide: Effectively Using Our Conclusion Generator

1) input the content you would like to include under "your text".

Simply write or paste the writing you would like to conclude. If you have a document or website you would like to upload, you can click the "Upload" button to import the text from a file or URL.

2) Add keywords

If you have specific key terms that should be included in your conclusion, you can specify them in the keywords field, separated by commas must be separated by commas.

3) Choose a length

Select a word count range for your conclusion, from a short paragraph of less than 50 words to a longer paragraph of 200-250.

4) Adjust settings (optional)

If you would like to tailor the style and formatting of your conclusion, click "Settings". Here you can select an option for tone, level of detail, target audience, and output language. You can also change the formatting from a paragraph to a bulleted or numbered list, and adjust the number of sections you would like to generate.

5) Click "Generate"

Click the generate button, and in a few moments your new conclusion will be added to the top of the feed.

6) Finetune Your Results

If you would like to rewrite individual sentences within a conclusion, use the Finetune feature. Simply click the Finetune button below a conclusion, and then select the sentence you would like paraphrased. Your conclusion will be added to the top of the feed with that sentence rewritten. Note that Finetune rewrites will count towards your number of free uses per day.

7) Use Your Paragraphs

Copy a conclusion by clicking the clipboard icon in the upper right, or download it as a .docx or .txt file by clicking the download icon in the same region.

What Powers Originality's Conclusion Generator?

Our AI Paragraph Conclusion Generator is equipped with the latest advancements in language technology, utilizing the GPT-3.5 system, a large language model (LLM) with 175 billion parameters. This powerful tool is engineered to produce high-quality writing that is tailored to your specific needs. It achieves this by analyzing the text you provide, and extracting any relevant key points or thesis statements, ensuring your conclusion aligns with your voice. The GPT-3.5 system's exceptional capability in interpreting language nuances guarantees that the generated content hits the core arguments of your writing. Moreover, its extensive training across multiple languages enables proficient writing and translation capabilities.

Other Tools You May Find Helpful

To assist you in creating exceptional content, Originality.ai has developed a cutting-edge set of free AI writing tools, built to tackle the biggest issues facing writers and content creators in a digital age. Need help writing body paragraphs? Try our  Already have a paragraph that needs adjusting? Try our paragraph generator . If you'd like to paraphrase rather than write from scratch, check out our paragraph rewriter .

Wherever you are in your writing journey, Originality.ai has you covered with AI tools that make content creation a breeze, for free.

Finish Strong with Originality.ai's Conclusion Generator

When navigating  the dynamic world of digital content creation, tools like Originality.ai's free conclusion generator aren't just a luxury—they're a necessity. Tailor-made for the needs of diverse content creators, this tool shines with its ability to produce satisfying conclusions for your writing.

Imagine boosting your productivity with lightning-fast efficiency while ensuring your content stands out in the competitive digital arena. That's exactly what Originality's conclusion generator offers. It's not just about staying afloat in the fast-paced world of online content; it's about soaring to new heights with the power of innovation and originality at your fingertips. Give it a try, end all your writing woes today!

Customers Love Originality.ai

We deeply understand your needs when it comes to identifying original content and we are building features around our accurate ai detection and plagiarism checking that users love.

After testing a number of AI content detection tools, I have found Originality.ai to be one of the best on the market . And now with the ability to detect paraphrased AI content, Orignality.ai is even more powerful. It’s basically my go-to detection tool at this point.

SEO Consultant, GSQI.com

At Clicking Publish, producing original, high-quality content is essential to our success. To maintain these standards, it's important that we verify the work from freelancers and outsourced writers. Originality.ai makes this process easy for us by providing a simple and efficient tool that ensures the content we receive meets our expectations.

Kityo Martin

Clicking Publish

I love the tool. Not only does it detect ACTUAL Al written content, but also writers who write just like Al. Great way to weed out Al and poor writing. Just because content was written by a human doesn't mean they did any better than an Al tool. We had a lot of our writers test positive for Al and they didn't use Al. What was common in all their writing was the lack of original thoughts. It was all regurgitation.

Ryan Cunningham

After doing some serious testing with Originality (which caters for the newerAl tech), I can't fool it (yet).

Founder, FatJoe

So what can we learn from this? In many cases, the tool tells the right story, even when it's nuanced, like in the case of AI content edited by humans.

Gael Breton

Founder, Authority Hacker

I realize that AI content isn't going away and with human editing, it can save time/make blog content better. That said, I've also had writers submit content that was 100% AI and never told me. A BIG no-no. This tool (Originality.ai) is what I'm using to stop that.

Ron Stefanski

OneHourProfessor.com

In The Press

Originality.ai has been featured for its accurate ability to detect gpt-3, chat gpt and gpt-4 generated content. see some of the coverage below…, featured by leading publications.

Originality.ai did a fantastic job on all three prompts, precisely detecting them as AI-written. Additionally, after I checked with actual human-written textual content, it did determine it as 100% human-generated, which is important.

Vahan Petrosyan

searchenginejournal.com

I use this tool most frequently to check for AI content personally. My most frequent use-case is checking content submitted by freelance writers we work with for AI and plagiarism.

searchengineland.com

After extensive research and testing, we determined Originality.AI to be the most accurate technology.

Rock Content Team

rockcontent.com

Jon Gillham, Founder of Originality.ai came up with a tool to detect whether the content is written by humans or AI tools. It’s built on such technology that can specifically detect content by ChatGPT-3 — by giving you a spam score of 0-100, with an accuracy of 94%.

Felix Rose-Collins

ranktracker.com

ChatGPT lacks empathy and originality. It’s also recognized as AI-generated content most of the time by plagiarism and AI detectors like Originality.AI

Ashley Stahl

Originality.AI Do give them a shot! 

Sri Krishna

venturebeat.com

For web publishers, Originality.AI will enable you to scan your content seamlessly , see who has checked it previously, and detect if an AI-powered tool was implored.

Industry Trends

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AI Content Detector & Plagiarism Checker for Serious Content Publishers

Improve your content quality by accurately detecting duplicate content and artificially generated text..

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Free Conclusion Generator Online

Get your essay conclusion finished with our AI-based tool. Get your conclusion paragraph perfect and unique!

Since it is an AI-based tool, final results may require additional work. To make things precise, we suggest using reliable professional help online!

How it Works?

• 1. Add your essay. Write title in the first box and then paste your essay into the second one.
• 2. You need to add at least 200 words to generate the conclusion part.
• 3. Click the button "Generate" to see the results. Enjoy.

Why You Should Try Our Conclusion Generator ?

• You receive a free analysis of the content that you provide to make a strong conclusion.
• Your grammar and spelling become perfect.
• Your thesis and title can be paraphrased to avoid repetition and self-plagiarism issues.
• Our free online conclusion generator works based on the style that you have provided.
• No need to register and share your personal information.

Get Your Essay Conclusion With Our Expert Writers

Approach reliable WritingBros help if you want to come up with a perfect conclusion or need to rewrite your essay draft as we can handle every write my conclusion problem. No need to stress regarding plagiarism or repetitions. Our experts know how to keep things unique by helping you create a strong conclusion, restate your thesis, and get things done in style! When you place your write my conclusion for me request as you hire an editor online, our writers know how to earn the best grades!

The Main Goals of The Conclusion Generating Tool

The importance of a conclusion generator these days cannot be underestimated. The final part of an essay paper or research writing is always time-consuming and it's hard to avoid repetitions as you sum things up. The most apparent risk is facing plagiarism risks or dealing with style problems. When you are getting used to your text, it is quite challenging to avoid repetitions or sentences that do not have a natural flow of words.

Turning to a free essay conclusion generator is a great way to receive certain ideas as you work on your paper and need inspiration. It takes about five minutes to copy and paste your existing idea or the thesis statement (up to 200 words) to receive automatic writing for your conclusion. We also let you check the grammar free of charge, so you do not have to proofread things in terms of grammar and style.

The Benefits of Using The Conclusion Generator Online

The most important benefit of using our conclusion paragraph generator is the availability of different styles, which means that you can work on a research paper, your scientific thesis, or a high school homework paper. The AI-based tool will analyze and generate your writing to come up with the perfect grammar and all the necessary sentences that will reflect what you have submitted. Don’t forget to include the most important part of your paper that will reflect your main thesis and things that you would like to have mentioned in your conclusion part.

The other benefits include plagiarism elimination as you use our conclusion paper solution. The tool is absolutely free and has no limitations in terms of how often you can try and edit the conclusion generator. This way, you can edit and see what changes appear. Click on the “Generate” button again and use it as much as you wish. The editing part is like working with a free pre-grader before you submit your essay or research paper. It won’t take you long and you do not have to violate your privacy. We care for your success and offer an intuitive solution to help make your writing unique!

How Our Free Conclusion Generator Works

When our conclusion tool receives the title of your paper and the content itself, it scans for the main message and the thesis by applying special rephrasing algorithms. It will create a special natural flow of words and sentences by removing those words that do not contain an important idea or do not provide essential content. Remember to include the main idea and think over your thesis. It is what has to be included.

Since it is an automatic conclusion writer, you should check and edit things twice to make sure that everything is in place. Add 1-2 sentences here and there to see what changes might take place if you want to achieve greater clarity. It always depends on what you would like to enter to see whether your conclusion is good. It always comes down to testing and evaluation! Give it a try since it’s totally free!

Manual Writing VS Conclusion Generator

Turning to the concluding sentence generator, you are saving yourself from stress and the resources wasted as you are editing and spending your precious time getting things done without any delays. When you are using our free tool, you are able to work with an actual draft that may require only minor editing. It will always depend on what you already have. The most important is that you do not have to focus on the grammar and the repetitions. Since there are self-plagiarism risks that you may face with manual writing, you will not have these challenges with the conclusion generator for essay composition that can help you overcome the writing block as you do not want to ruin your original idea.

Our clincher generator processes your content and uses similar verbs and other adjectives to keep the main message while keeping it unique.

When you’re dealing with an automatic conclusion writer, you can edit things manually and get the best of both worlds by proofreading and adjusting your writing. It’s still much faster and easier. You already have your idea paraphrased, so there are no plagiarism issues. Adding natural flow to your writing, you will always stand out!

What Makes a Really Good Conclusion?

Every essay or research paper that is qualified to receive an A+ should follow the primary rules of the paper's type. In most cases, it will have a brief summary and the main message of the paper with the thesis statement that is restated. Once again, it will always depend on what essay types you are planning to provide. For example, writing a conclusion for essay for some research paper, you may provide further reading or any research recommendations.

If you are working on an environmental essay, it will be useful to provide a call to action in your final part. Our conclusion sentence generator will always examine the source and see what kind of conclusion is most efficient. You can apply additional editing to make things perfect. This is what helps you to analyze things by saving your time since there are already closing paragraph sentences available.

Remember that your conclusion should reflect the main idea and make it even more accessible. Our conclusion sentence maker is flexible and will provide you with suggestions that you may either approve or ignore.

Every conclusion should be unique and it's essential to avoid plagiarism as you write even if you only repeat what has already been mentioned in your sources.

As you might know, there should be no new ideas because if it has not been mentioned in your paper, they should not be there. This is exactly where our concluding paragraph generator will help you to achieve success. It uses paraphrasing based only on what you already have, thus eliminating the risks. When you are working on a complex paper and the use of words that you already have is overly challenging, the use of our free solution will help you to adjust and improve the readability. Even if you are feeling stuck and do not know how to continue, you can paste anything up to 200 words to see the results and find some inspiration. Give it a good try, summarize things until the perfect rating, and never have to worry about plagiarism or the lack of readability again!

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How to write a conclusion paragraph with AI

Take the hassle out of writing conclusions with Hypotenuse AI conclusion generator. We help you summarize key points to generate a satisfying conclusion.

Add your topic

To get started, simply describe your topic or paste in your content. You can also add keywords to further guide our AI, or choose a tone for your content.

Quickly and easily generate conclusions

AI will generate an initial conclusion paragraph summarizing the main ideas and key findings depending on the details you provide.

Review & edit

Once you've generated your copy, our tool will produce some unique variations for you to choose from. Simply choose the one you like best, edit it and use it however you like.

Make Powerful Conclusions Instantly with AI

Time-saving and easy.

Save time and energy by eliminating the need to write a conclusion from scratch. Hypotenuse AI automates the task for you, in a fraction of the time it takes to write it manually.

Benefit-Driven Conclusions

Generate conclusions that are designed to drive results. Each conclusion goes beyond basic summarization and is crafted to be high-impact, benefit-driven, and effective.

Customizable Results

You’re in control. Create multiple conclusion variations to test out different approaches or structures and determine which works best for your unique needs.

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A conclusion generator is a powerful tool that helps writers create effective and impactful conclusions for their articles or other types of writing. It analyzes the content and context of the document and generates relevant and well-crafted concluding statements.

A conclusion generator is designed to support various types of articles, including informative essays, persuasive essays, research papers, reports, and more. Regardless of the subject or topic, a conclusion generator can assist in crafting a strong and compelling conclusion that aligns with the purpose and tone of the document.

To make a conclusion for an informative essay, summarize the main points discussed in the essay and highlight the key takeaways that readers should remember. This helps to reinforce the main information and provide a quick recap.

When making a conclusion in a persuasive essay, begin by restating the main argument or claim presented in the essay. Summarize the key supporting points that validate the argument and remind readers of the main persuasive points.

When it comes to creating conclusions, Hypotenuse AI truly stands out as the best option available. As an AI tool, it harnesses the power of advanced machine learning models to analyze the text and understand the key points, topics, and overall meaning.

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How a Conclusion Generator Can Transform Your Writing

You've done the hard work — you've researched your topic, crafted a compelling argument, and written an essay that flows logically from point to point. But now, you're stuck. Summarizing your key ideas and bringing your essay to a strong conclusion seems impossible. Your mind goes blank. The conclusion is so crucial, but after pouring hours into the rest of the essay, your creativity and motivation have evaporated.

Sound familiar? Don't worry — you're not alone. Writing conclusions is tough for everyone at some point. But there's an easy solution that can save you time and boost the power of your ending: use a conclusion generator. With the click of a button, an AI can analyze your essay and instantly provide a choice of possible conclusions tailored to your content and audience.

What Is a Conclusion Generator?

A conclusion generator is a tool that can automatically generate a conclusion for your article. Many writers struggle with writing conclusions, so a generator can be very helpful.

How Does a Conclusion Generator Work?

Conclusion generators typically use machine learning and natural language processing to analyze your article and generate an appropriate conclusion. The generator will read through your entire article, identify the main topics or arguments, and craft a conclusion that wraps up your key points while reaffirming your ideas. AI also checks for consistency in tone and style to ensure the conclusion flows well with the rest of your writing.

The generated conclusion will not be exactly the same as what you might write yourself, but it can give you a good starting point. You can then modify or rewrite the conclusion as needed to match your particular writing style and strengthen the ending of your article.

How to write a good conclusion

So you've crafted an amazing introduction, and built up your argument or story throughout the body of your writing, and now it's time to wrap it all up. Your conclusion should reinforce the key points you've made and leave the reader with a lasting impression. Here are some tips for writing a strong conclusion:

Restate your main argument

Briefly reiterate your main argument or the key takeaway from your writing. Remind the reader why your topic matters or the significance of your findings. You want the conclusion to feel connected to the introduction.

Summarize your main points

Go over the main points, examples, or steps covered in the body section, but don't just repeat yourself word for word. Keep this high level without too many details. Use different phrasing to reinforce the key messages.

End with impact

Finish strong with a statement that leaves an impression on the reader. You might end with a warning, call the reader to action, issue a challenge, or suggest implications to consider. Pose a thought-provoking question or paint a vivid picture with a metaphor. For greater effect, you can refer back to the opening anecdote or scenario.

Keep it concise

A conclusion should only be 3 to 5 sentences for a short article. Don't drag it on or introduce new ideas. Reiterate your main points concisely while tying it all back to your thesis. The conclusion brings closure, so avoid an abrupt ending.

Benefits of Using a Conclusion Generator

There are several benefits to using an automated conclusion generator:

Coming up with an impactful conclusion can be time-consuming. A generator can draft one in just a few seconds.

Provides inspiration

If you're stuck, a generated conclusion can spark some ideas to get you started.

Check for consistency

An AI tool will analyze your entire essay and generate a conclusion that aligns with the overall style, tone, and message. This helps ensure consistency throughout your work.

Improves your own writing

Seeing different examples of effective conclusions, even those written by an AI, can strengthen your own conclusion-writing skills over time. You may pick up some useful phrases or techniques to incorporate into your future essays.

How to Use a Conclusion Generator Effectively

Using a conclusion generator is a simple way to transform your writing and take it to the next level. Here are some tips for using one effectively:

Choose Relevant Options

Generate multiple conclusions and pick those most relevant for your particular piece of writing. Ignore anything too generic or that seems out of place. For the best results, you want a conclusion specifically tailored to your subject matter and target audience.

Customize as Needed

Once you choose a conclusion, you may need to tweak or build upon it to make it perfect. Add in specific examples or statistics from your work to strengthen the conclusion. You can also reword parts of it to better reflect your unique writing style and voice. Think of the generator's suggestions as a starting point, not the final say.

Vary Your Conclusions

Don't rely on the same stock conclusion for every piece of writing. Mix it up by using different options from the generator, or alternating between its suggestions and your own original conclusions.

Carefully proofread your conclusion, as with any part of your writing. Ensure the concluding statement is logically and cohesively tied to the rest of your work. Get feedback from others if possible. Your conclusion is your final opportunity to drive your message home to readers, so you want it to be as polished and compelling as possible.

Example conclusion prompts

Some common conclusion prompts a generator may provide include:

• In summary, ...
• In conclusion, ...
• All in all, ...
• To sum up, ...

Starting your concluding paragraph with a prompt like these helps signal to the reader that you're bringing your main points together and wrapping up your work.

From there, the generator will offer examples to help spark your creativity. For instance, if your essay was on the benefits of daily exercise, conclusion examples may be:

• In summary, by exercising each day you can lead a healthier, happier, and more productive life. The benefits to both physical and mental well-being are enormous. Make the time for physical activity and start reaping the rewards today.
• All in all, daily exercise has significant physical and mental benefits that impact your health, mood, and quality of life. Staying active and fit leads to both short and long-term rewards that enrich how you live each day. Establishing the habit of regular exercise is challenging, but the outcomes are well worth the effort.

Using a prompt and tweaking one of these examples is an easy way to draft your conclusion. Be sure to connect it back to your main points and reiterate why your topic matters. Keep the tone consistent and end with a call to action if appropriate.

Use Cases of Conclusion Generator

• Academic Writing: For students, scholars, and researchers, it makes academic writing easier. Summarizing complex research into clear conclusions makes papers more understandable and impactful.
• Business Reports: In business, concise conclusions are important. A conclusion generator helps professionals end reports strongly. It emphasizes the most useful insights and drives home the core message to decision-makers.
• Content Creation: Content creators also find conclusion generators useful. For articles and blog posts, it wraps up the content engagingly. This reinforces the message and encourages readers to interact. That helps achieve the goals of the content.

Write Satisfying Conclusions with AI

So there you have it, a conclusion generator can be an incredibly useful tool for any writer. It helps ensure you end on a high note and leave your readers with a lasting impression. The ending is what people remember, so make it count. Give Hypotenuse AI a try — you've got nothing to lose and a memorable conclusion to gain. Your writing will thank you, and so will your readers. What are you waiting for? Go start crafting conclusions that convert and see how much of a difference it can make.

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1 Copy the text you need to summarize.

2 Insert your text into the conclusion summarizer box.

3 Click “Summarize” and create final words for your essay!

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No emails required and the comparable view is available. You can use all types of papers in the conclusion sentence generator and get results in 5 seconds.

Free Conclusion Generator For Your Essay

Struggling to find the perfect ending for your essay? Our innovative Conclusion Generator is here to help. Designed for students, academics, and writers, this tool simplifies the process of concluding your essays with just a few clicks.

Powered by Advanced AI: How Our Conclusion Generator Works

Our Conclusion Generator is more than just a simple tool. It's a sophisticated AI that understands your text's context, tone, and style, ensuring your conclusion aligns perfectly with your essay. Here's how it transforms your writing experience:

• 📊 Deep Learning Algorithms : At its core, our tool uses advanced deep learning algorithms. These algorithms have been trained on a vast corpus of academic and creative writing, enabling the AI to understand various writing styles, tones, essay types and structures.
• 📝 Contextual Understanding : Unlike simpler tools, our generator grasps the context of your essay. It analyzes the main arguments, key points, and the overall message to create a conclusion that aligns seamlessly with your content.
• ✍️ Tone Matching : Whether your essay is formal, informal, or creative, our tool adapts its output to match. This ensures that the tone of the conclusion is consistent with the rest of your writing.
• ☝️ Synthesis of Key Points : By synthesizing the key points of your essay, the AI crafts a summary that not only encapsulates your main arguments but also adds a compelling final touch to your narrative.
• ✨ Plagiarism-Free and Unique : Every conclusion generated is unique and plagiarism-free, providing you with original content that pass any plagiarism checker , and enhances your original writing.
• ⏳ Continuous Learning and Improvement : Our AI is designed to learn continuously. With every use, it becomes more adept at generating high-quality conclusions, ensuring that you always get the best results.

Maximize Your Essay's Impact with More GradeFixer’s Tools

Facing challenges in essay writing? GradeFixer is here to assist beyond just concluding your essays. Our platform offers an extensive database of free essay samples , serving as a valuable resource for research and inspiration. Whether you're drafting an essay or seeking topic ideas , these samples can provide the guidance you need.

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If you're looking for that extra edge in your university journey, GradeFixer's free conclusion paragraph generator, essay title generator , and our other resources are just a click away. Embrace a smoother academic path and achieve success with GradeFixer's comprehensive suite of tools and support.

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• Deadlines Met At Every Stage
• Unique And Plagiarism Free

Concluding Sentence Generator

We perfectly understand how difficult it can be to write the concluding words for an essay. You might feel like you have said everything that could be said, and you want to be done already. Our experts want to help you accomplish your task. Using our concluding sentence generator will save you a ton of time and effort. Merely insert your text into the given window and press the button!

If you want to practice writing conclusions yourself, then it is essential that you read our article. Here, we have outlined the most vital elements of working with final paragraphs. We have explained what they should contain and what to avoid.

✔️ A Concluding Paragraph: 5 Rules to Follow

For every original work, you need to come up with a unique ending. Yet, there are several general rules that you should apply when writing them. Our concluding paragraph generator will help you start the process, but you still need to learn how to craft your paper’s last segment.

To ensure your essay conclusion is well-presented, follow these rules:

• First of all, you should connect your conclusion to your introduction . Restate your thesis from the beginning in just a sentence. It should come out concise and easy to understand, even if the reader didn’t dive into the body paragraphs.
• Do not repeat the findings word to word but provide a synthesis of arguments . Show how your ideas offer the answer to the research problem you have raised in the introduction. You can have a reasonable amount of repetitions. Don’t be afraid to use keywords everywhere they fit as they serve as “topic threads” throughout your paper.
• Most importantly, explain the significance of your findings . The last paragraph is the perfect place to address why your arguments and results are crucial. Keep in mind that a conclusion only takes 5% of the paper’s length. Thus, emphasize the main points and ideas shortly, without exploring the details.
• To show the quality of your work, indicate future opportunities . In your conclusion, highlight why you think further research is needed. It will also serve as evidence that you understand the issue entirely. Your readers will appreciate this bridge between the current and subsequent studies. For instance, you can do it by showing “the gap” in your research. Or you can situate it in a different context or culture.
• Last but not least, use clear and straightforward language . The last sentence can create an impactful impression on your reader. So, make sure to use expressive language when creating it.

❗ Things to Avoid in a Concluding Statement

A lot has been written about what goes into the essay. Sometimes it is better to know what to avoid, thus eliminating conflicting tips. In this section, we have enumerated students’ most common mistakes when writing their conclusions.

To successfully write a conclusion paragraph, AVOID…

• Sounding like a cliché. Starting your conclusion with phrases like “in conclusion” and “in summary” is not the best idea. They do sound a bit too cliche. Aim to find a transition so that the final paragraph starts naturally. Try to bring your paper to another level by making your conclusion stand out.
• Including any new information. Avoid bringing new ideas, quotes, and arguments in your concluding paragraph. Additionally, make sure to write it in your own words. You can’t repeat the phrasing of anything previously said.
• Repeating yourself over and over. Simply repeating previous parts of your paper will make the conclusion look dull. The better strategy is rewriting the arguments . Tell how all your points are interconnected and relate to the thesis.
• Being too vague. Do not end your conclusion with statements that are too ambiguous. Take this paragraph as an opportunity to strengthen your paper. Thus, don’t waste your time on generic phrases that add nothing of value.
• Writing at the last minute. Do not leave the summary to the last second. Even if you are unsure how to approach it, write your thoughts down anyway. It is easier to work with the draft rather than with nothing. You can also create bullet points about what you want to discuss on a separate paper.

Thank you for your attention! Maybe, our concluding statement generator isn’t what you were looking for. If that's the case, you should peruse our free online summary generator tool . It will create a short synopsis of any text!

Updated: Apr 5th, 2024

🔗 References

• Organizing Your Social Sciences Research Paper: the Conclusion – Research Guides at University of Southern California
• Six Things to AVOID in Your Conclusion – David J. Klooster Center for Excellence in Writing, Hope College
• Academic Writing: “In Conclusion”...How Not to End Your Paper – SFU Library, Simon Fraser University

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Free Online Conclusion Generator

How to succeed using the concluding paragraph generator we offer? With our service, you can make a conclusion for your paper or assigned reading in a few easy steps.

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You might be wondering how this conclusion paragraph generator can create content sufficient for your essay. So, how exactly does this software work? We encourage you to try our concluding statement generator and promise that you'll not be disappointed.

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Conclusion Maker Filled With Benefits

We have designed this ideal conclusion paragraph maker for those who may struggle with writing a final section for their paper. Thousands of students worldwide have already tested our software, and we couldn't find any negative feedback about it. Know why you should choose our software.

Online conclusion generator free access makes your writing more straightforward and effective. No payment for the service.

There is no need to wait for days. With our essay conclusion maker, you will get your piece of content in a few seconds.

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The free conclusion generator also does not collect any personal data. It does not share any information or your papers.

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Who Can Use a Conclusion Paragraph Generator?

A free conclusion paragraph generator can be used for various purposes. It can be helpful not only for students.

Use a free essay conclusion generator to submit your assignments in time. We know how long it can take to write a quality paper. Make your study more convenient with the essay conclusion typer we offer!

Use this tool as a research paper conclusion generator for your own articles or for the readings you need for the research. It is accurate with data and main ideas from papers, analyses, and manuscripts.

A great chance to apply our conclusion maker for essays you work with. It will bring a fresh view to your text, make it readable, and create insights for advanced writers. Works with different types, styles, and academic requirements.

Create a conclusion for articles you write to see how much time our tool can save you. The software will analyze the text and make short, relevant, proficient sum paragraphs. Use this tool for faster reading of documents for your investigation.

Our conclusion summary generator is an advanced tool for text editing. It analyzes all the paragraphs and defines the most critical points from the text. Make your text better with the easy software solutions we offer.

Online Conclusion Generator Tool

We are proud to say that our tool is the best conclusion generator for essay, papers, research, or other documents you work with. We build this conclusion tool to solve the problems students usually battle with. There are always a lot of assignments, tons of readings, and various types of academic papers to submit. So, it is hard, especially if you also have a full-time job. We created a conclusion generator tool to make your life easier. Why is it essential to use an accurate conclusion generator? You should ensure that you get the text with all crucial insights from the sample. Also, you need a finalized text in good condition. This is what we offer our clients – fast results, accurate text, and good quality of writing.

Conclusion Maker for Essay and All Kinds of Papers

A paper conclusion generator can help with different types of work in academia. Writing 20-30 pages of academic text can be stressful every week. We create a conclusion generator for essay to simplify your educational journey. This software can be applied to various types of academic work and majors. For example, an argumentative essay conclusion generator will provide a text that fits all the requirements for this type of writing.  Looking who can “write my conclusion paragraph”? It can be a complicated task but quite achievable! We can create a final section for any type of academic work:

• argumentative essay
• research paper
• narrative essay
• analytical paper
• critical article
• book review, and others.

An automatic conclusion writer generator is the best academic choice to provide an understandable and readable text.  What can make your paper better? A solid final paragraph will make your readers think or navigate their own insights. This is an advanced project for researchers. People will often read the introduction and review section of your paper. That is why those parts should be obvious and accurate. We provide an excellent opportunity to have essay help 24/7.

Essay Conclusion Generator Helping With Assignments

Writing assignments for college and high school is probably the most common thing. We encourage you to use the conclusion generator for an essay and submit all your papers and texts on time. Our team works with students of all levels. Our expertise helped us with creating a conclusion builder.  Using a concluding paragraph maker can reduce overall writing time. Let our software make the final touches to your paper while you dedicate some free time to something more enjoyable. If the deadline is pressing and you opt for impeccable result, StudyCrumb also offers you professional assistance with any scholarly work. Just say " write my research paper " and we will handle your request. Need dissertation help ? No problem! Looking for term papers for sale ? Visit StudyCrumb! Essay conclusion generator free tool can become your best helper in getting a degree or graduating from school. This tool was designed specifically for students' needs. It is easy to navigate, always accessible, and works even with very complicated topics. Math? Biology? Social Science? Anything you need can be covered by our tool! Believe it or not, an excellent concluding paragraph for your essay is just one click away! Students love the software we created for them. Check the testimonial section for proof.  Need some help with the final paragraphs? Working with complicated assignments and can't finalize them? Check our tool for professional service!

How Our Concluding Paragraph Generator Works

You may wonder how it became possible to create a conclusion paragraph generator free tool that provides quality text? There is no big deal for being hesitant if you are a first-time user. We will explain how the algorithm of the conclusion essay generator works and why it's suitable for any type of paper.  The core of this software is AI, machine learning technology. It analyzes the text and improves results based on your personal requirements. You may wonder, who can “write my conclusion paragraph for me”? Entrust this work to AI tools! However, if you have or " write my college essay " inquiry, turning to our professionals will be a better option. What can be easier? To create a conclusion paragraph builder, we synthesize the best world practices and technologies. It analyzes written text outlining key points and messages of its content. Past the text, you need to recap, click the "generate" button, and get the results in a few seconds. It's as easy as that! Do not spend a lot of time on this writing part, trust our service!

Why Use Our Conclusion Sentence Generator

We designed a conclusion sentence maker helping millions of students successfully graduate from schools and universities. What makes us believe this is one of the most wanted tools for academic writing assignments?  Have you ever felt drained or exhausted after completing almost your whole essay with only one last paragraph left? If so, it's precisely why you should try concluding sentence maker or summarizer as an alternative. It will craft a perfect ending for your paper in no time.  Let's define what makes our conclusion paragraph tool one of the best helpers in studying.

• Easy to use There is no need to learn to code or create custom solutions. All you need to do is put your text into the field and get results in a moment.
• Free to use Conclusion writer free tool is accessible to students worldwide, making this product the most liked. No registration or data collection. Open the website and start working.
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Find Out The Most Trustworthy AI Description Generators [Review]

AI description generators are important in many ways. It can help users, especially those people in the business industry. As we all know, businesses must know how to promote their products and services in catchy content. One of the best ways to attract clients or customers is to create an excellent description of a certain product. However, if you are finding it hard to add a description to the product manually, then it is best to use an AI-powered tool that can assist you with your task. Thankfully, this post will offer a detailed review of the best AI description generators you can use. With this, you can add a description of a certain product, along with the job description, art description, and more. So, what are you waiting for? Read this review and learn all the capabilities of the best picks AI description generator .

Part 1. How to Select The Best AI Description Generator

Part 2. top 6 ai description generator, part 3. best tool to use for brainstorming before writing descriptions, part 4. faqs about ai description generators.

There are various things you need to consider when selecting the best and most helpful AI description generators. So, to give you an idea, you can do the following:

◆ Consider your budget and needs. You must know what type of description you need.

◆ You can experience the tool if it has a free trial or demo, which is more helpful for exploring its capabilities.

◆ You also need to compare all the AI tools you have found out. Then, see what tool suits you.

◆ Determine the strengths and weaknesses of each AI tool.

◆ Look for the ratings and reviews. It may help you see the experience of other users.

◆ If you are dealing with sensitive product data, always ensure that the AI tool you use has a proper security measure.

If you want the best AI image description generator, use Ahrefs . This AI-powered tool can help you generate a description automatically after you add the image to its main interface. Also, the generation process is fast, making it an ideal AI tool for everyone. Plus, you can choose your preferred writing tone, such as formal, casual, diplomatic, professional, friendly, and more. It also supports various languages, like English, French, German, Spanish, and more.

◆ Generating product description.

◆ Promoting and marketing.

◆ Advertising.

◆ Ranking.

Key Functions:

◆ Generate description automatically.

◆ Adding writing tone.

◆ Producing three descriptions simultaneously.

Limitations

◆ There are times when the tool produces a description that is not related to the image.

◆ It requires a strong internet connection to perform well.

The next tool you can consider as your AI product description generator is Copy AI . With this online tool, you can generate various descriptions easily and instantly. It is because the tool has a simple layout and fast generation process, making it an amazing tool for all users. Plus, the tool can provide up to two descriptions in a single click. With that, you can have two options to choose your preferred product description.

◆ Boost marketing.

◆ Content creation.

◆ Brainstorming.

◆ The free version has limited features.

◆ It requires a human touch to ensure that the content is accurate.

3. Writesonic

If you are looking for another AI product description generator, then we would like to introduce Writesonic . After operating this AI-powered tool, we have found out that it is among the best and most reliable tools for generating descriptions for your product. The generation procedure is also fast, making it an ideal AI tool. Aside from that, you can choose how many words you want. You also need to insert the image and put some helpful prompts in the text box. It is also more helpful to add various keywords to make your product attractive and unique in the eyes of other people.

◆ Product description.

◆ Content writing.

◆ Marketing copy.

◆ Blog post.

◆ Generating product descriptions smoothly.

◆ Create various content.

◆ Plagiarism checking.

4. Simplified

Another great AI description generator for free is Simplified . With the help of this tool, you can make a description in just a few minutes. All you need is to insert all the information from the box. Also, you can attach the image or the product you have. With that, Simplified will begin doing magic to generate a catchy description. After getting the result, it’s up to you if you want to do some revision and enhance the content to make it better and unique.

◆ Craft engaging social media content.

◆ Promoting and marketing products.

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◆ Generating different contents.

◆ Integrates seamlessly with Google Docs.

◆ It has a limited range of content types.

◆ It is not totally free. To generate lengthy content, get the paid version.

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◆ Checking various contents.

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◆ Generating job descriptions.

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◆ It needs a subscription plan.

◆ An Internet connection is highly recommended.

In this article

Can you generate juice wrld's voice using ai, concerns when using ai voice generators, how to use a juice wrld ai voice generator, how to enrich your juice wrld's ai voice, bonus tool for improving your content: wondershare democreator.

Absolutely! Numerous tools will allow you to generate your favorite rapper's voice . These tools use the power of artificial intelligence to thoroughly examine speech and singing patterns, providing the most accurate results.

After using these machine learning systems, the tools can successfully generate any text or convert your voice into Juice Wrld's. Some of these tools are so advanced that you won't notice a difference between the real voice and the AI-generated one.

Since AI voice generators are relatively new on the market, you may have some concerns while trying to copy the voice of your favorite rapper. We'll name some of the most common concerns below to give you insight into your journey.

• Authenticity – you may not get the most accurate and lifelike results if you use AI voice generators to replicate Juice Wrld's voice.
• Originality – the content you produce may not be original and cause additional issues, such as the uniqueness of human performances.
• Copyright laws – if you share your generated voices on social media platforms and thus threaten someone's identity and copyright, you may face serious legal issues.
• Misuse – you should never use AI voice generators to impersonate or steal someone's identity since it's considered to be a crime.

Top 5 Juice Wrld AI Voice Generators

Below is a list of the best Juice Wrld AI voice generators available today. They all provide authentic Juice Wrld voices that meet your needs and expectations. We'll present each tool to help you select the ideal solution.

1. TopMediai

TopMediai is one of the most powerful AI voice generators available, currently providing over 3200 realistic voices.

Moreover, the tool is available in over 190 languages, making it one of the most inclusive and convenient options. You can use TopMediai to generate voices from text or change the voices from your recordings. Thanks to the new features, you can also create unique songs.

One downside of this tool is that you can only download your audio files in the WAV format, and you must convert them later if you want to use another format type. On the bright side, it's a web-based tool, so you won't have to download additional tools and take up space on your device to generate Juice Wrld's voice.

VoxBox is a simple and sophisticated generation and cloning tool powered by AI. It is downloadable, and if you install it on your device, you can access advanced editing features, such as pitch, pause, emphasis, and more. The platform currently provides over 3000 celebrity voices, including Juice Wrld and other renowned rap artists.

Moreover, the tool can copy around 100 accents, which is rare in the AI voice generator market. The downside of this tool is that you'll have to pay to access the celebrity voices, so don't expect to get Juice Wrld content for free on VoxBox.

Kits.ai is a hybrid AI voice generator, meaning that you can use the web-based option or download the tool to your desktop device and start cloning. The tool provides numerous features and generation options, such as training a voice, creating a unique voice, removing vocals from audio files, and many more for the best audio experience.

The program, however, only works with audio files, and you won't be able to generate Juice Wrld's voice through the text. You'll have to record your message to get the desired results or use a YouTube video as a reference. You'll also have to pay to use the celebrities' voices on Kits.ai.

4. Jammable

Jammable, also known as Voicify, was designed specially to create AI covers using your favorite celebrity's voice.

It's mostly used for song covers, but you can also upload a conversational audio clip and watch the magic happen before your eyes. The platform is well-organized, and you can filter the voices according to the category for the most convenient journey.

One of the most unique features of this platform is that you can create duets. For instance, you can create a track with Juice Wrld and another famous rapper for incredible song covers. Once you generate your Juice Wrld voice, use integrated editing tools to take your recordings to the next level.

FakeYou is one of the most popular AI voice generators on the market, and it is used by beginners and professionals in the AI field.

The tool provides numerous features and options, such as text-to-voice, voice-to-voice, and even face animators to create professional recordings. As the name suggests, this deep fake tool will provide the most accurate Juice Wrld results.

One of the things we didn't like on this platform is that there's a queue, and you have to wait before you get your recording. People who have purchased premium plans will have priority, so if you're a free user, you may have to wait before generating Juice Wrld's voice.

All these Juice Wrld AI voice generators are beginner-friendly and easy to use. However, if you experience issues, the general guide below can help you navigate the tool and get the desired results.

For this guide, we'll use TopMediai. It's the perfect balance between all AI generator tools, so you can learn how to navigate them all from a single guide. It's also one of the fastest and most potent Juice Wrld tools on the market, which is why it stands out. Now, let's jump into the guide:

• Now, input the text you want to generate.

Let's be real: most of the abovementioned AI voice generators won't provide the most amazing results. You'll have to rely on post-production editing to make your audio clips come to life and create the best outcome. We've prepared some tips and tricks on how to enrich your Juice Wrld content and master the art of AI voice generators.

• Remove background noise – sometimes, your audio files may have background noise, leading to distracting and unprofessional results. Instead, you can use advanced editing tools to remove these noises and elevate your experience.
• Add subtitles/captions – by incorporating these elements into your recordings, you'll make the content more accessible and reach a larger audience.
• Add audio effects – effects such as audio fade in and fade out can take your Juice Wrld audio files to the next level and engage the listeners even more.
• Change the audio speed – since AI generators may provide slower or faster celebrity recordings, you can change the audio speed and get the most accurate and authentic content.

The tips and tricks above can elevate your experience, but performing all these actions individually may be complex, especially if you're new to the audio editing industry.

We present the most universal tool for improving your Juice Wrld content: Wondershare DemoCreator .

This feature-packed tool will give you everything you need to film and edit your juice wrld AI voice and get the most accurate results. You can check out some of the features below:

• AI denoiser – this feature removes background noises within seconds. Thanks to this robust tool, you won't have to remove audio elements from your Juice Wrld AI voice manually.
• Auto Subtitles – by adding auto subtitles, you'll make the recordings more accessible. Moreover, subtitles may be crucial for rap songs as they may be faster and harder to understand.
• Fade-in and fade-out – it provides some of the best audio effects on the market, including fade-in and fade-out that will surely make your audio files climb the competitive ladder.
• Audio speed settings – since the audio may not be the right speed to replicate Juice Wrld's speech, you can adjust the speed settings by using this tool.

The list of features goes on. One feature that will soon be added to the library is an advanced AI voice changer. The voice changer will provide numerous celebrity voices, such as Elon Musk, Juice Wrld, and more, so you'll get an all-in-one tool capable of generating and editing your audio files.

Juice Wrld's legacy lives on, and the army of fans keeps growing daily. Many fans want to preserve their memories of him and are searching for trustworthy AI voice generators to create unique text and hear his voice again. We've prepared the best Juice Wrld AI voice generators to help you reach your goals.

This guide focuses on everything you need to know to generate fantastic Juice Wrld audio files, including how to enhance the quality of your content. If you don't want to edit your audio manually, you can check out Wondershare DemoCreator and use the power of AI to elevate your recordings.

• Remove hiss and hums from Juice Wrld AI voice in seconds with AI denoiser
• Use AI speech enhancer to make Juice Wrld voices stand out
• Use Auto-subtitles to match lyrics with the timeline

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• Open access
• Published: 13 May 2024

SCIPAC: quantitative estimation of cell-phenotype associations

• Dailin Gan 1 ,
• Yini Zhu 2 ,
• Xin Lu 2 , 3 &
• Jun Li   ORCID: orcid.org/0000-0003-4353-5761 1  

Genome Biology volume  25 , Article number:  119 ( 2024 ) Cite this article

175 Accesses

2 Altmetric

Metrics details

Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p -value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC’s accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.

Single-cell RNA sequencing (scRNA-seq) technologies are revolutionizing biomedical research by providing comprehensive characterizations of diverse cell populations in heterogeneous tissues [ 1 , 2 ]. Unlike bulk RNA sequencing (RNA-seq), which measures the average expression profile of the whole tissue, scRNA-seq gives the expression profiles of thousands of individual cells in the tissue [ 3 , 4 , 5 , 6 , 7 ]. Based on this rich data, cell types may be discovered/determined in an unsupervised (e.g., [ 8 , 9 ]), semi-supervised (e.g., [ 10 , 11 , 12 , 13 ]), or supervised manner (e.g., [ 14 , 15 , 16 ]). Despite the fast development, there are still limitations with scRNA-seq technologies. Notably, the cost for each scRNA-seq experiment is still high; as a result, most scRNA-seq data are from a single or a few biological samples/tissues. Very few datasets consist of large numbers of samples with different phenotypes, e.g., cancer vs. normal. This places great difficulties in determining how a cell type contributes to a phenotype based on single-cell studies (especially if the cell type is discovered in a completely unsupervised manner or if people have limited knowledge of this cell type). For example, without having single-cell data from multiple cancer patients and multiple normal controls, it could be hard to computationally infer whether a cell type may promote or inhibit cancer development. However, such association can be critical for cancer research [ 17 ], disease diagnosis [ 18 ], cell-type targeted therapy development [ 19 ], etc.

Fortunately, this difficulty may be overcome by borrowing information from bulk RNA-seq data. Over the past decade, a considerable amount of bulk RNA-seq data from a large number of samples with different phenotypes have been accumulated and made available through databases like The Cancer Genome Atlas (TCGA) [ 20 ] and cBioPortal [ 21 , 22 ]. Data in these databases often contain comprehensive patient phenotype information, such as cancer status, cancer stages, survival status and time, and tumor metastasis. Combining single-cell data from a single or a few individuals and bulk data from a relatively large number of individuals regarding a particular phenotype can be a cost-effective way to determine how a cell type contributes to the phenotype. A recent method Scissor [ 23 ] took an essential step in this direction. It uses single-cell and bulk RNA-seq data with phenotype information to classify the cells into three discrete categories: Scissor+, Scissor−, and null cells, corresponding to cells that are positively associated, negatively associated, and not associated with the phenotype.

Here, we present a method that takes another big step in this direction, which is called Single-Cell and bulk data-based Identifier for Phenotype Associated Cells or SCIPAC for short. SCIPAC enables quantitative estimation of the strength of association between each cell in a scRNA-seq data and a phenotype, with the help of bulk RNA-seq data with phenotype information. Moreover, SCIPAC also enables the estimation of the statistical significance of the association. That is, it gives a p -value for the association between each cell and the phenotype. Furthermore, SCIPAC enables the estimation of association between cells and an ordinal phenotype (e.g., different stages of cancer), which could be informative as people may not only be interested in the emergence/existence of cancer (cancer vs. healthy, a binary problem) but also in the progression of cancer (different stages of cancer, an ordinal problem).

To study the performance of SCIPAC, we first apply SCIPAC to simulated data under three schemes. SCIPAC shows high accuracy with low false positive rates. We further show the broad applicability of SCIPAC on real datasets across various diseases, including prostate cancer, breast cancer, lung cancer, and muscular dystrophy. The association inferred by SCIPAC is highly informative. In real datasets, some cell types have definite and well-studied functions, while others are less well-understood: their functions may be disease-dependent or tissue-dependent, and they may contain different sub-types with distinct functions. In the former case, SCIPAC’s results agree with current biological knowledge. In the latter case, SCIPAC’s discoveries inspire the generation of new hypotheses regarding the roles and functions of cells under different conditions.

An overview of the SCIPAC algorithm

SCIPAC is a computational method that identifies cells in single-cell data that are associated with a given phenotype. This phenotype can be binary (e.g., cancer vs. normal), ordinal (e.g., cancer stage), continuous (e.g., quantitative traits), or survival (i.e., survival time and status). SCIPAC uses input data consisting of three parts: single-cell RNA-seq data that measures the expression of p genes in m cells, bulk RNA-seq data that measures the expression of the same set of p genes in n samples/tissues, and the statuses/values of the phenotype of the n bulk samples/tissues. The output of SCIPAC is the strength and the p -value of the association between each cell and the phenotype.

SCIPAC proposes the following definition of “association” between a cell and a phenotype: A group of cells that are likely to play a similar role in the phenotype (such as cells of a specific cell type or sub-type, cells in a particular state, cells in a cluster, cells with similar expression profiles, or cells with similar functions) is considered to be positively/negatively associated with a phenotype if an increase in their proportion within the tissue likely indicates an increased/decreased probability of the phenotype’s presence. SCIPAC assigns the same association to all cells within such a group. Taking cancer as the phenotype as an example, if increasing the proportion of a cell type indicates a higher chance of having cancer (binary), having a higher cancer stage (ordinal), or a higher hazard rate (survival), all cells in this cell type is positively associated with cancer.

The algorithm of SCIPAC follows the following four steps. First, the cells in the single-cell data are grouped into clusters according to their expression profiles. The Louvain algorithm from the Seurat package [ 24 , 25 ] is used as the default clustering algorithm, but the user may choose any clustering algorithm they prefer. Or if information of the cell types or other groupings of cells is available a priori, it may be supplied to SCIPAC as the cell clusters, and this clustering step can be skipped. In the second step, a regression model is learned from bulk gene expression data with the phenotype. Depending on the type of the phenotype, this model can be logistic regression, ordinary linear regression, proportional odds model, or Cox proportional hazards model. To achieve a higher prediction power with less variance, by default, the elastic net (a blender of Lasso and ridge regression [ 26 ]) is used to fit the model. In the third step, SCIPAC computes the association strength \(\Lambda\) between each cell cluster and the phenotype based on a mathematical formula that we derive. Finally, the p -values are computed. The association strength and its p -value between a cell cluster and the phenotype are given to all cells in the cluster.

SCIPAC requires minimum tuning. When the cell-type information is given in step 1, SCIPAC does not have any (hyper)parameter. Otherwise, the Louvain algorithm used in step 1 has a “resolution” parameter that controls the number of cell clusters: a larger resolution results in more clusters. SCIPAC inherits this parameter as its only parameter. Since SCIPAC gives the same association strength and p -value to cells from the same cluster, this parameter also determines the resolution of results provided by SCIPAC. Thus, we still call it “resolution” in SCIPAC. Because of its meaning, we recommend setting it so that the number of cell clusters given by the clustering algorithm is comparable to, or reasonably larger than, the number of cell types (or sub-types) in the data. We will see that the performance of SCIPAC is insensitive to this resolution parameter, and the default value 2.0 typically works well.

The details of the SCIPAC algorithm are given in the “ Methods ” section.

Performance in simulated data

We assess the performance of SCIPAC in simulated data under three different schemes. The first scheme is simple and consists of only three cell types. The second scheme is more complicated and consists of seven cell types, which better imitates actual scRNA-seq data. In the third scheme, we simulate cells under different cell development stages to test the performance of SCIPAC under an ordinal phenotype. Details of the simulation are given in Additional file 1.

Simulation scheme I

Under this scheme, the single-cell data consists of three cell types: one is positively associated with the phenotype, one is negatively associated, and the third is not associated (we call it “null”). Figure 1 a gives the UMAP [ 27 ] plot of the three cell types, and Fig. 1 b gives the true associations of these three cell types with the phenotype, with red, blue, and light gray denoting positive, negative, and null associations.

UMAP visualization and numeric measures of the simulated data under scheme I. All the plots in a–e  are scatterplots of the two dimensional single-cell data given by UMAP. The x and y axes represent the two dimensions, and their scales are not shown as their specific values are not directly relevant. Points in the plots represents single cells, and they are colored differently in each subplot to reflect different information/results. a  Cell types. b  True associations. The association between cell types 1, 2, and 3 and the phenotype are positive, negative, and null, respectively. c  Association strengths \(\Lambda\) given by SCIPAC under different resolutions. Red/blue represents the sign of \(\Lambda\) , and the shade gives the absolute value of \(\Lambda\) . Every cell is colored red or blue since no \(\Lambda\) is exactly zero. Below each subplot, Res stands for resolution, and K stands for the number of cell clusters given by this resolution. d   p -values given by SCIPAC. Only cells with p -value \(< 0.05\) are colored red (positive association) or blue (negative association); others are colored white. e  Results given by Scissor under different \(\alpha\) values. Red, blue, and light gray stands for Scissor+, Scissor−, and background (i.e., null) cells. f  F1 scores and g  FSC for SCIPAC and Scissor under different parameter values. For SCIPAC, each bar is the value under a resolution/number of clusters. For Scissor, each bar is the value under an \(\alpha\)

We apply SCIPAC to the simulated data. For the resolution parameter (see the “ Methods ” section), values 0.5, 1.0, and 1.5 give 3, 4, and 4 clusters, respectively, close to the actual number of cell types. They are good choices based on the guidance for choosing this parameter. To show how SCIPAC behaves under parameter misspecification, we also set the resolution up to 4.0, which gives a whopping 61 clusters. Figure 1 c and d give the association strengths \(\Lambda\) and the p -values given by four different resolutions (results under other resolutions are provided in Additional file 1: Fig. S1 and S2). In Fig. 1 c, red and blue denote positive and negative associations, respectively, and the shade of the color represents the strength of the association, i.e., the absolute value of \(\Lambda\) . Every cell is colored blue or red, as none of \(\Lambda\) is exactly zero. In Fig. 1 d, red and blue denote positive and negative associations that are statistically significant ( p -value \(< 0.05\) ). Cells whose associations are not statistically significant ( p -value \(\ge 0.05\) ) are shown in white. To avoid confusion, it is worth repeating that cells that are colored in red/blue in Fig. 1 c are shown in red/blue in Fig. 1 d only if they are statistically significant; otherwise, they are colored white in Fig. 1 d.

From Fig. 1 c, d (as well as Additional file 1: Fig. S1 and S2), it is clear that the results of SCIPAC are highly consistent under different resolution values, including both the estimated association strengths and the p -values. It is also clear that SCIPAC is highly accurate: most truly associated cells are identified as significant, and most, if not all, truly null cells are identified as null.

As the first algorithm that quantitatively estimates the association strength and the first algorithm that gives the p -value of the association, SCIPAC does not have a real competitor. A previous algorithm, Scissor, is able to classify cells into three discrete categories according to their associations with the phenotype. So, we compare SCIPAC with Scissor in respect of the ability to differentiate positively associated, negatively associated, and null cells.

Running Scissor requires tuning a parameter called \(\alpha\) , which is a number between 0 and 1 that balances the amount of regularization for the smoothness and for the sparsity of the associations. The Scissor R package does not provide a default value for this \(\alpha\) or a function to help select this value. The Scissor paper suggests the following criterion: “the number of Scissor-selected cells should not exceed a certain percentage of total cells (default 20%) in the single-cell data. In each experiment, a search on the above searching list is performed from the smallest to the largest until a value of \(\alpha\) meets the above criteria.” In practice, we have found that this criterion does not often work properly, as the truly associated cells may not compose 20% of all cells in actual data. Therefore, instead of setting \(\alpha\) to any particular value, we set \(\alpha\) values that span the whole range of \(\alpha\) to see the best possible performance of Scissor.

The performance of Scissor in our simulation data under four different \(\alpha\) values are shown in Fig. 1 e, and results under more \(\alpha\) values are shown in Additional file 1: Fig. S3. In the figures, red, blue, and light gray denote Scissor+, Scissor−, and null (called “background” in Scissor) cells, respectively. The results of Scissor have several characteristics different from SCIPAC. First, Scissor does not give the strength or statistical significance of the association, and thus the colors of the cells in the figures do not have different shades. Second, different \(\alpha\) values give very different results. Greater \(\alpha\) values generally give fewer Scissor+ and Scissor− cells, but there are additional complexities. One complexity is that the Scissor+ (or Scissor−) cells under a greater \(\alpha\) value are not a strict subset of Scissor+ (or Scissor−) cells under a smaller \(\alpha\) value. For example, the number of truly negatively associated cells detected as Scissor− increases when \(\alpha\) increases from 0.01 to 0.30. Another complexity is that the direction of the association may flip as \(\alpha\) increases. For example, most cells of cell type 2 are identified as Scissor+ under \(\alpha =0.01\) , but many of them are identified as Scissor− under larger \(\alpha\) values. Third, Scissor does not achieve high power and low false-positive rate at the same time under any \(\alpha\) . No matter what the \(\alpha\) value is, there is only a small proportion of cells from cell type 2 that are correctly identified as negatively associated, and there is always a non-negligible proportion of null cells (i.e., cells from cell type 3) that are incorrectly identified as positively or negatively associated. Fourth, Scissor+ and Scissor− cells can be close to each other in the figure, even under a large \(\alpha\) value. This means that cells with nearly identical expression profiles are detected to be associated with the phenotype in opposite directions, which can place difficulties in interpreting the results.

SCIPAC overcomes the difficulties of Scissor and gives results that are more informative (quantitative strengths with p -values), more accurate (both high power and low false-positive rate), less sensitive to the tuning parameter, and easier to interpret (cells with similar expression typically have similar associations to the phenotype).

SCIPAC’s higher accuracy in differentiating positively associated, negatively associated, and null cells than Scissors can also be measured numerically using the F1 score and the fraction of sign correctness (FSC). F1, which is the harmonic mean of precision and recall, is a commonly used measure of calling accuracy. Note that precision and recall are only defined for two-class problems, which try to classify desired signals/discoveries (so-called “positives”) against noises/trivial results (so-called “negatives”). Our case, on the other hand, is a three-class problem: positive association, negative association, and null. To compute F1, we combine the positive and negative associations and treat them as “positives,” and treat null as “negatives.” This F1 score ignores the direction of the association; thus, it alone is not enough to describe the performance of an association-detection algorithm. For example, an algorithm may have a perfect F1 score even if it incorrectly calls all negative associations positive. To measure an algorithm’s ability to determine the direction of the association, we propose a statistic called FSC, defined as the fraction of true discoveries that also have the correct direction of the association. The F1 score and FSC are numbers between 0 and 1, and higher values are preferred. A mathematical definition of these two measures is given in Additional file 1.

Figure 1 f, g show the F1 score and FSC of SCIPAC and Scissor under different values of tuning parameters. The F1 score of Scissor is between 0.2 and 0.7 under different \(\alpha\) ’s. The FSC of Scissor increases from around 0.5 to nearly 1 as \(\alpha\) increases, but Scissor does not achieve high F1 and FSC scores at the same time under any \(\alpha\) . On the other hand, the F1 score of SCIPAC is close to perfection when the resolution parameter is properly set, and it is still above 0.90 even if the resolution parameter is set too large. The FSC of SCIPAC is always above 0.96 under different resolutions. That is, SCIPAC achieves high F1 and FSC scores simultaneously under a wide range of resolutions, representing a much higher accuracy than Scissor.

Simulation scheme II

This more complicated simulation scheme has seven cell types, which are shown in Fig. 2 a. As shown in Fig. 2 b, cell types 1 and 3 are negatively associated (colored blue), 2 and 4 are positively associated (colored red), and 5, 6, and 7 are not associated (colored light gray).

UMAP visualization of the simulated data under a–g  scheme II and h–k  scheme III. a  Cell types. b  True associations. c , d  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. e  Results given by Scissor under different \(\alpha\) values. f  F1 scores and g  FSC for SCIPAC and Scissor under different parameter values. h  Cell differentiation paths. The four paths have the same starting location, which is in the center, but different ending locations. This can be considered as a progenitor cell type differentiating into four specialized cell types. i  Cell differentiation steps. These steps are used to create four stages, each containing 500 steps. Thus, this plot of differentiation steps can also be viewed as the plot of true association strengths. j , k  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution

The association strengths and p -values given by SCIPAC under the default resolution are illustrated in Fig. 2 c, d, respectively. Results under several other resolutions are given in Additional file 1: Fig. S4 and S5. Again, we find that SCIPAC gives highly consistent results under different resolutions. SCIPAC successfully identifies three out of the four truly associated cell types. For the other truly associated cell type, cell type 1, SCIPAC correctly recognizes its association with the phenotype as negative, although the p -values are not significant enough. The F1 score is 0.85, and the FSC is greater than 0.99, as shown in Fig. 2 f, g.

The results of Scissor under four different \(\alpha\) values are given in Fig. 2 e. (More shown in Additional file 1: Fig. S6.) Under this highly challenging simulation scheme, Scissor can only identify one out of four truly associated cell types. Its F1 score is below 0.4.

Simulation scheme III

This simulation scheme is to assess the performance of SCIPAC for ordinal phenotypes. We simulate cells along four cell-differentiation paths with the same starting location but different ending locations, as shown in Fig. 2 h. These cells can be considered as a progenitor cell population differentiating into four specialized cell types. In Fig. 2 i, the “step” reflects their position in the differentiation path, with step 0 meaning the start and step 2000 meaning the end of the differentiation. Then, the “stage” is generated according to the step: cells in steps 0 \(\sim\) 500, 501 \(\sim\) 1000, 1001 \(\sim\) 1500, and 1501 \(\sim\) 2000 are assigned to stages I, II, III, and IV, respectively. This stage is treated as the ordinal phenotype. Under this simulation scheme, Fig. 2 i also gives the actual associations, and all cells are associated with the phenotype.

The results of SCIPAC under the default resolution are shown in Fig. 2 j, k. Clearly, the associations SCIPAC identifies are highly consistent with the truth. Particularly, it successfully identifies the cells in the center as early-stage cells and most cells at the end of branches as last-stage cells. The results of SCIPAC under other resolutions are given in Additional file 1: Fig. S7 and S8, which are highly consistent. Scissor does not work with ordinal phenotypes; thus, no results are reported here.

Performance in real data

We consider four real datasets: a prostate cancer dataset, a breast cancer dataset, a lung cancer dataset, and a muscular dystrophy dataset. The bulk RNA-seq data of the three cancer datasets are obtained from the TCGA database, and that of the muscular dystrophy dataset is obtained from a published paper [ 28 ]. A detailed description of these datasets is given in Additional file 1. We will use these datasets to assess the performance of SCIPAC on different types of phenotypes. The cell type information (i.e., which cell belongs to which cell type) is available for the first three datasets, but we ignore this information so that we can make a fair comparison with Scissor, which cannot utilize this information.

Prostate cancer data with a binary phenotype

We use the single-cell expression of 8,700 cells from prostate-cancer tumors sequenced by [ 29 ]. The cell types of these cells are known and given in Fig. 3 a. The bulk data comprises 550 TCGA-PRAD (prostate adenocarcinoma) samples with phenotype (cancer vs. normal) information. Here the phenotype is cancer, and it is binary: present or absent.

UMAP visualization of the prostate cancer data, with a zoom-in view for the red-circled region (cell type MNP). a  True cell types. BE, HE, and CE stand for basal, hillock, club epithelial cells, LE-KLK3 and LE-KLK4 stand for luminal epithelial cells with high levels of kallikrein related peptidase 3 and 4, and MNP stands for mononuclear phagocytes. In the zoom-in view, the sub-types of MNP cells are given. b  Association strengths \(\Lambda\) given by SCIPAC under the default resolution. The cyan-circled cells are B cells, which are estimated by SCIPAC as negatively associated with cancer but estimated by Scissor as Scissor+ or null. c   p -values given by SCIPAC. The MNP cell type, which is red-circled in the plot, is estimated by SCIPAC to be strongly negatively associated with cancer but estimated by Scissor to be positively associated with cancer. d  Results given by Scissor under different \(\alpha\) values

Results from SCIPAC with the default resolution are shown in Fig. 3 b, c (results with other resolutions, given in Additional file 1: Fig. S9 and S10, are highly consistent with results here.) Compared with results from Scissor, shown in Fig. 3 d, results from SCIPAC again show three advantages. First, results from SCIPAC are richer and more comprehensive. SCIPAC gives estimated associations and the corresponding p -values, and the estimated associations are quantitative (shown in Fig. 3 b as different shades to the red or blue color) instead of discrete (shown in Fig. 3 d as a uniform shade to the red, blue, or light gray color). Second, SCIPAC’s results can be easier to interpret as the red and blue colors are more block-wise instead of scattered. Third, unlike Scissor, which produces multiple sets of results varying based on the parameter \(\alpha\) —a parameter without a default value or tuning guidance—typically, a single set of results from SCIPAC under its default settings suffices.

Comparing the results from our SCIPAC method with those from Scissor is non-trivial, as the latter’s outcomes are scattered and include multiple sets. We propose the following solutions to summarize the inferred association of a known cell type with the phenotype using a specific method (Scissor under a specific \(\alpha\) value, or SCIPAC with the default setting). We first calculate the proportion of cells in this cell type identified as Scissor+ (by Scissor at a specific \(\alpha\) value) or as significantly positively associated (by SCIPAC), denoted by \(p_{+}\) . We also calculate the proportion of all cells, encompassing any cell type, which are identified as Scissor+ or significantly positively associated, serving as the average background strength, denoted by \(p_{a}\) . Then, we compute the log odds ratio for this cell type to be positively associated with the phenotype compared to the background, represented as:

Similarly, the log odds ratio for the cell type to be negatively associated with the phenotype, \(\rho _-\) , is computed in a parallel manner.

For SCIPAC, a cell type is summarized as positively associated with the phenotype if \(\rho _+ \ge 1\) and \(\rho _- < 1\)  and negatively associated if \(\rho _- \ge 1\) and \(\rho _+ < 1\) . If neither condition is met, the association is inconclusive. For Scissor, we apply it under six different \(\alpha\) values: 0.01, 0.05, 0.10, 0.15, 0.20, and 0.25. A cell type is summarized as positively associated with the phenotype if \(\rho _+ \ge 1\) and \(\rho _- < 1\) in at least four of these \(\alpha\) values and negatively associated if \(\rho _- \ge 1\) and \(\rho _+ < 1\) in at least four \(\alpha\) values. If these criteria are not met, the association is deemed inconclusive. The above computation of log odds ratios and the determination of associations are performed only on cell types that each compose at least 1% of the cell population, to ensure adequate power.

For the prostate cancer data, the log odds ratios for each cell type using each method are presented in Tables S1 and S2. The final associations determined for each cell type are summarized in Table S3. In the last column of this table, we also indicate whether the conclusions drawn from SCIPAC and Scissor are consistent or not.

We find that SCIPAC’s results agree with Scissor on most cell types. However, there are three exceptions: mononuclear phagocytes (MNPs), B cells, and LE-KLK4.

MNPs are red-circled and zoomed in in each sub-figure of Fig. 3 . Most cells in this cell type are colored red in Fig. 3 d but colored dark blue in Fig. 3 b. In other words, while Scissor determines that this cell type is Scissor+, SCIPAC makes the opposite inference. Moreover, SCIPAC is confident about its judgment by giving small p -values, as shown in Fig. 3 c. To see which inference is closer to the biological fact is not easy, as biologically MNPs contain a number of sub-types that each have different functions [ 30 , 31 ]. Fortunately, this cell population has been studied in detail in the original paper that generated this dataset [ 29 ], and the sub-type information of each cell is provided there: this MNP population contains six sub-types, which are dendritic cells (DC), M1 macrophages (Mac1), metallothionein-expressing macrophages (Mac-MT), M2 macrophages (Mac2), proliferating macrophages (Mac-cycling), and monocytes (Mono), as shown in the zoom-in view of Fig. 3 a. Among these six sub-types, DC, Mac1, and Mac-MT are believed to inhibit cancer development and can serve as targets in cancer immunotherapy [ 29 ]; they compose more than 60% of all MNP cells in this dataset. SCIPAC makes the correct inference on this majority of MNP cells. Another cell type, Mac2, is reported to promote tumor development [ 32 ], but it only composes less than \(15\%\) of the MNPs. How the other two cell types, Mac-cycling and Mono, are associated with cancer is less studied. Overall, the results given by SCIPAC are more consistent with the current biological knowledge.

B cells are cyan-circled in Fig. 3 b. B cells are generally believed to have anti-tumor activity by producing tumor-reactive antibodies and forming tertiary lymphoid structures [ 29 , 33 ]. This means that B cells are likely to be negatively associated with cancer. SCIPAC successfully identifies this negative association, while Scissor fails.

LE-KLK4, a subtype of cancer cells, is thought to be positively associated with the tumor phenotype [ 29 ]. SCIPAC successfully identified this positive association, in contrast to Scissor, which failed to do so (in the figure, a proportion of LE-KLK4 cells are identified as Scissor+, especially under the smallest \(\alpha\) value; however, this proportion is not significantly higher than the background Scissor+ level under the majority of \(\alpha\) values).

In summary, across all three cell types, the results from SCIPAC appear to be more consistent with current biological knowledge. For more discussions regarding this dataset, refer to Additional file 1.

Breast cancer data with an ordinal phenotype

The scRNA-seq data for breast cancer are from [ 34 ], and we use the 19,311 cells from the five HER2+ tumor tissues. The true cell types are shown in Fig. 4 a. The bulk data include 1215 TCGA-BRCA samples with information on the cancer stage (I, II, III, or IV), which is treated as an ordinal phenotype.

UMAP visualization of the breast cancer data. a  True cell types. CAFs stand for cancer-associated fibroblasts, PB stands for plasmablasts and PVL stands for perivascular-like cells. b , c  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. Cyan-circled are a group of T cells that are estimated by SCIPAC to be most significantly associated with the cancer stage in the negative direction, and orange-circled are a group of T cells that are estimated by SCIPAC to be significantly positively associated with the cancer stage. d  DE analysis of the cyan-circled T cells vs. all the other T cells. e  DE analysis of the cyan-circled T cells vs. all the other cells. f  Expression of CD8+ T cell marker genes in the cyan-circled cells and all the other cells. g  DE analysis of the orange-circled T cells vs. all the other cells. h  Expression of regulatory T cell marker genes in the orange-circled cells and all the other cells

Association strengths and p -values given by SCIPAC under the default resolution are shown in Fig. 4 b, c. Results under other resolutions are given in Additional file 1: Fig. S11 and S12, and again they are highly consistent with results under the default resolution. We do not present the results from Scissor, as Scissor does not take ordinal phenotypes.

In the SCIPAC results, cells that are most strongly and statistically significantly associated with the phenotype in the positive direction are the cancer-associated fibroblasts (CAFs). This finding agrees with the literature: CAFs contribute to therapy resistance and metastasis of cancer cells via the production of secreted factors and direct interaction with cancer cells [ 35 ], and they are also active players in breast cancer initiation and progression [ 36 , 37 , 38 , 39 ]. Another large group of cells identified as positively associated with the phenotype is the cancer epithelial cells. They are malignant cells in breast cancer tissues and are thus expected to be associated with severe cancer stages.

Of the cells identified as negatively associated with severe cancer stages, a large portion of T cells is the most noticeable. Biologically, T cells contain many sub-types, including CD4+, CD8+, regulatory T cells, and more, and their functions are diverse in the tumor microenvironment [ 40 ]. To explore SCIPAC’s discoveries, we compare T cells that are identified as most statistically significant, with p -values \(< 10^{-6}\) and circled in Fig. 4 d, with the other T cells. Differential expression (DE) analysis (details about DE analysis and other analyses are given in Additional file 1) identifies seven genes upregulated in these most significant T cells. Of these seven genes, at least five are supported by the literature: CCL4, XCL1, IFNG, and GZMB are associated with CD8+ T cell infiltration; they have been shown to have anti-tumor functions and are involved in cancer immunotherapy [ 41 , 42 , 43 ]. Also, IL2 has been shown to serve an important role in combination therapies for autoimmunity and cancer [ 44 ]. We also perform an enrichment analysis [ 45 ], in which a pathway called Myc stands out with a \(\textit{p}\text{-value}<10^{-7}\) , much smaller than all other pathways. Myc is downregulated in the T cells that are identified as most negatively associated with cancer stage progress. This agrees with current biological knowledge about this pathway: Myc is known to contribute to malignant cell transformation and tumor metastasis [ 46 , 47 , 48 ].

On the above, we have compared T cells that are most significantly associated with cancer stages in the negative direction with the other T cells using DE and pathway analysis, and the results could suggest that these cells are tumor-infiltrated CD8+ T cells with tumor-inhibition functions. To check this hypothesis, we perform DE analysis of these cells against all other cells (i.e., the other T cells and all the other cell types). The DE genes are shown in Fig. 4 e. It can be noted that CD8+ T cell marker genes such as CD8A, CD8B, and GZMK are upregulated. We further obtain CD8+ T cell marker genes from CellMarker [ 49 ] and check their expression, as illustrated in Fig. 4 f. Marker genes CD8A, CD8B, CD3D, GZMK, and CD7 show significantly higher expression in these T cells. This again supports our hypothesis that these cells are tumor-infiltrated CD8+ T cells that have anti-tumor functions.

Interestingly, not all T cells are identified as negatively associated with severe cancer stages; a group of T cells is identified as positively associated, as circled in Fig. 4 c. To explore the function of this group of T cells, we perform DE analysis of these T cells against the other T cells. The DE genes are shown in Fig. 4 g. Based on the literature, six out of eight over-expressed genes are associated with cancer development. The high expression of NUSAP1 gene is associated with poor patient overall survival, and this gene also serves as a prognostic factor in breast cancer [ 50 , 51 , 52 ]. Gene MKI67 has been treated as a candidate prognostic prediction for cancer proliferation [ 53 , 54 ]. The over-expression of RRM2 has been linked to higher proliferation and invasiveness of malignant cells [ 55 , 56 ], and the upregulation of RRM2 in breast cancer suggests it to be a possible prognostic indicator [ 57 , 58 , 59 , 60 , 61 , 62 ]. The high expression of UBE2C gene always occurs in cancers with a high degree of malignancy, low differentiation, and high metastatic tendency [ 63 ]. For gene TOP2A, it has been proposed that the HER2 amplification in HER2 breast cancers may be a direct result of the frequent co-amplification of TOP2A [ 64 , 65 , 66 ], and there is a high correlation between the high expressions of TOP2A and the oncogene HER2 [ 67 , 68 ]. Gene CENPF is a cell cycle-associated gene, and it has been identified as a marker of cell proliferation in breast cancers [ 69 ]. The over-expression of these genes strongly supports the correctness of the association identified by SCIPAC. To further validate this positive association, we perform DE analysis of these cells against all the other cells. We find that the top marker genes obtained from CellMarker [ 49 ] for the regulatory T cells, which are known to be immunosuppressive and promote cancer progression [ 70 ], are over-expressed with statistical significance, as shown in Fig. 4 h. This finding again provides strong evidence that the positive association identified by SCIPAC for this group of T cells is correct.

Lung cancer data with survival information

The scRNA-seq data for lung cancer are from [ 71 ], and we use two lung adenocarcinoma (LUAD) patients’ data with 29,888 cells. The true cell types are shown in Fig. 5 a. The bulk data consist of 576 TCGA-LUAD samples with survival status and time.

UMAP visualization of a–d  the lung cancer data and e–g  the muscular dystrophy data. a  True cell types. b , c  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. d  Results given by Scissor under different \(\alpha\) values. e , f  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. Circled are a group of cells that are identified by SCIPAC as significantly positively associated with the disease but identified by Scissor as null. g  Results given by Scissor under different \(\alpha\) values

Association strengths and p -values given by SCIPAC are given in Fig. 5 b, c (results under other resolutions are given in Additional file 1: Fig. S13 and S14). In Fig. 5 c, most cells with statistically significant associations are CD4+ T cells or B cells. These associations are negative, meaning that the abundance of these cells is associated with a reduced death rate, i.e., longer survival time. This agrees with the literature: CD4+ T cells primarily mediate anti-tumor immunity and are associated with favorable prognosis in lung cancer patients [ 72 , 73 , 74 ]; B cells also show anti-tumor functions in all stages of human lung cancer development and play an essential role in anti-tumor responses [ 75 , 76 ].

The results by Scissor under different \(\alpha\) values are shown in Fig. 5 d. The highly scattered Scissor+ and Scissor− cells make identifying and interpreting meaningful phenotype-associated cell groups difficult.

Muscular dystrophy data with a binary phenotype

This dataset contains cells from four facioscapulohumeral muscular dystrophy (FSHD) samples and two control samples [ 77 ]. We pool all the 7047 cells from these six samples together. The true cell types of these cells are unknown. The bulk data consists of 27 FSHD patients and eight controls from [ 28 ]. Here the phenotype is FSHD, and it is binary: present or absent.

The results of SCIPAC with the default resolution are given in Fig. 5 e, f. Results under other resolutions are highly similar (shown in Additional file 1: Fig. S15 and S16). For comparison, results given by Scissor under different \(\alpha\) values are presented in Fig. 5 g. The agreements between the results of SCIPAC and Scissor are clear. For example, both methods identify cells located at the top and lower left part of UMAP plots to be negatively associated with FSHD, and cells located at the center and right parts of UMAP plots to be positively associated. However, the discrepancies in their results are also evident. The most pronounced one is a large group of cells (circled in Fig. 5 f) that are identified by SCIPAC as significantly positively associated but are completely ignored by Scissor. Checking into this group of cells, we find that over 90% (424 out of 469) come from the FSHD patients, and less than 10% come from the control samples. However, cells from FSHD patients only compose 73% (5133) of all the 7047 cells. This statistically significant ( p -value \(<10^{-15}\) , Fisher’s exact test) over-representation (odds ratio = 3.51) suggests that the positive association identified SCIPAC is likely to be correct.

SCIPAC is computationally highly efficient. On an 8-core machine with 2.50 GHz CPU and 16 GB RAM, SCIPAC takes 7, 24, and 2 s to finish all the computation and give the estimated association strengths and p -values on the prostate cancer, lung cancer, and muscular dystrophy datasets, respectively. As a reference, Scissor takes 314, 539, and 171 seconds, respectively.

SCIPAC works with various phenotype types, including binary, continuous, survival, and ordinal. It can easily accommodate other types by using a proper regression model with a systematic component in the form of Eq. 3 (see the “ Methods ” section). For example, a Poisson or negative binomial log-linear model can be used if the phenotype is a count (i.e., non-negative integer).

In SCIPAC’s definition of association, a cell type is associated with the phenotype if increasing the proportion of this cell type leads to a change of probability of the phenotype occurring. The strength of association represents the extent of the increase or decrease in this probability. In the case of binary-response, this change is measured by the log odds ratio. For example, if the association strength of cell type A is twice that of cell type B, increasing cell type A by a certain proportion leads to twice the amount of change in the log odds ratio of having the phenotype compared to increasing cell type B by the same proportion. The association strength under other types of phenotypes can be interpreted similarly, with the major difference lying in the measure of change in probability. For quantitative, ordinal, and survival outcomes, the difference in the quantitative outcome, log odds ratio of the right-tail probability, and log hazard ratio respectively are used. Despite the differences in the exact form of the association strength under different types of phenotypes, the underlying concept remains the same: a larger (absolute value of) association strength indicates that the same increase/decrease in a cell type leads to a larger change in the occurrence of the phenotype.

As SCIPAC utilizes both bulk RNA-seq data with phenotype and single-cell RNA-seq data, the estimated associations for the cells are influenced by the choice of the bulk data. Although different bulk data can yield varying estimations of the association for the same single cells, the estimated associations appear to be reasonably robust even when minor changes are made to the bulk data. See Additional file 1 for further discussions.

When using the Louvain algorithm in the Seurat package to cluster cells, SCIPAC’s default resolution is 2.0, larger than the default setting of Seurat. This allows for the identification of potential subtypes within the major cell type and enables the estimation of individual association strengths. Consequently, a more detailed and comprehensive description of the association between single cells and the phenotype can be obtained by SCIPAC.

When applying SCIPAC to real datasets, we made a deliberate choice to disregard the cell annotation provided by the original publications and instead relied on the inferred cell clusters produced by the Louvain algorithm. We made this decision for several reasons. Firstly, we aimed to ensure a fair comparison with Scissor, as it does not utilize cell-type annotations. Secondly, the original annotation might not be sufficiently comprehensive or detailed. Presumed cell types could potentially encompass multiple subtypes, each of which may exhibit distinct associations with the phenotype under investigation. In such cases, employing the Louvain algorithm with a relatively high resolution, which is the default setting in SCIPAC, enables us to differentiate between these subtypes and allows SCIPAC to assign varying association strengths to each subtype.

SCIPAC fits the regression model using the elastic net, a machine-learning algorithm that maximizes a penalized version of the likelihood. The elastic net can be replaced by other penalized estimates of regression models, such as SCAD [ 78 ], without altering the rest of the SCIPAC algorithm. The combination of a regression model and a penalized estimation algorithm such as the elastic net has shown comparable or higher prediction power than other sophisticated methods such as random forests, boosting, or neural networks in numerous applications, especially for gene expression data [ 79 ]. However, there can still be datasets where other models have higher prediction power. It will be future work to incorporate these models into SCIPAC.

The use of metacells is becoming an efficient way to handle large single-cell datasets [ 80 , 81 , 82 , 83 ]. Conceptually, SCIPAC can incorporate metacells and their representatives as an alternative to its default setting of using cell clusters/types and their centroids. We have explored this aspect using metacells provided by SEACells [ 81 ]. Details are given in Additional file 1. Our comparative analysis reveals that combining SCIPAC with SEACells results in significantly reduced performance compared to using SCIPAC directly on original single-cell data. The primary reason for this appears to be the subpar performance of SEACells in cell grouping, especially when contrasted with the Louvain algorithm. Given these findings, we do not suggest using metacells provided by SEACells for SCIPAC applications in the current stage.

Conclusions

SCIPAC is a novel algorithm for studying the associations between cells and phenotypes. Compared to the previous algorithm, SCIPAC gives a much more detailed and comprehensive description of the associations by enabling a quantitative estimation of the association strength and by providing a quality control—the p -value. Underlying SCIPAC are a general statistical model that accommodates virtually all types of phenotypes, including ordinal (and potentially count) phenotypes that have never been considered before, and a concise and closed-form mathematical formula that quantifies the association, which minimizes the computational load. The mathematical conciseness also largely frees SCIPAC from parameter tuning. The only parameter (i.e., the resolution) barely changes the results given by SCIPAC. Overall, compared with its predecessor, SCIPAC represents a substantially more capable software by being much more informative, versatile, robust, and user-friendly.

The improvement in accuracy is also remarkable. In simulated data, SCIPAC achieves high power and low false positives, which is evident from the UMAP plot, F1 score, and FSC score. In real data, SCIPAC gives results that are consistent with current biological knowledge for cell types whose functions are well understood. For cell types whose functions are less studied or more multifaceted, SCIPAC gives support to certain biological hypotheses or helps identify/discover cell sub-types.

SCIPAC’s identification of cell-phenotype associations closely follows its definition of association: when increasing the fraction of a cell type increases (or decreases) the probability for a phenotype to be present, this cell type is positively (or negatively) associated with the phenotype.

The increase of the fraction of a cell type

For a bulk sample, let vector \(\varvec{G} \in \mathbb {R}^p\) be its expression profile, that is, its expression on the p genes. Suppose there are K cell types in the tissue, and let \(\varvec{g}_{k}\) be the representative expression of the k ’th cell type. Usually, people assume that \(\varvec{G}\) can be decomposed by

where \(\gamma _{k}\) is the proportion of cell type k in the bulk tissue, with \(\sum _{k = 1}^{K}\gamma _{k} = 1\) . This equation links the bulk and single-cell expression data.

Now consider increasing cells from cell type k by \(\Delta \gamma\) proportion of the original number of cells. Then, the new proportion of cell type k becomes \(\frac{\gamma _{k} + \Delta \gamma }{1 + \Delta \gamma }\) , and the new proportion of cell type \(j \ne k\) becomes \(\frac{\gamma _{j}}{1 + \Delta \gamma }\)  (note that the new proportions of all cell types should still add up to 1). Thus, the bulk expression profile with the increase of cell type k becomes

Plugging Eq. 1 , we get

Interestingly, this expression of \(\varvec{G}^*\) does not include \(\gamma _{1}, \ldots , \gamma _{K}\) . This means that there is no need actually to compute \(\gamma _{1}, \ldots , \gamma _{K}\) in Eq. 1 , which could otherwise be done using a cell-type-decomposition software, but an accurate and robust decomposition is non-trivial [ 84 , 85 , 86 ]. See Additional file 1 for a more in-depth discussion on the connections of SCIPAC with decomposition/deconvolution.

The change in chance of a phenotype

In this section, we consider how the increase in the fraction of a cell type will change the chance for a binary phenotype such as cancer to occur. Other types of phenotypes will be considered in the next section.

Let \(\pi (\varvec{G})\) be the chance of an individual with gene expression profile \(\varvec{G}\) for this phenotype to occur. We assume a logistic regression model to describe the relationship between \(\pi (\varvec{G})\) and \(\varvec{G}\) :

here the left-hand side is the log odds of \(\pi (\varvec{G})\) , \(\beta _{0}\) is the intercept, and \(\varvec{\beta }\) is a length- p vector of coefficients. In the section after the next, we will describe how we obtain \(\beta _{0}\) and \(\varvec{\beta }\) from the data.

When increasing cells from cell type k by \(\Delta \gamma\) , \(\varvec{G}\) becomes \(\varvec{G}^*\) in Eq. 3 . Plugging Eq. 2 , we get

We further take the difference between Eqs. 4 and 3 and get

The left-hand side of this equation is the log odds ratio (i.e., the change of log odds). On the right-hand side, \(\frac{\Delta \gamma }{1 + \Delta \gamma }\) is an increasing function with respect to \(\Delta \gamma\) , and \(\varvec{\beta }^T(\varvec{g}_{k} - \varvec{G})\) is independent of \(\Delta \gamma\) . This indicates that given any specific \(\Delta \gamma\) , the log odds ratio under over-representation of cell type k is proportional to

\(\lambda _k\) describes the strength of the effect of increasing cell type k to a bulk sample with expression profile \(\varvec{G}\) . Given the presence of numerous bulk samples, employing multiple \(\lambda _k\) ’s could be cumbersome and obscure the overall effect of a particular cell type. To concisely summarize the association of cell type k , we propose averaging their effects. The average effect on all bulk samples can be obtained by

where \(\bar{\varvec{G}}\) is the average expression profile of all bulk samples.

\(\Lambda _k\) gives an overall impression of how strong the effect is when cell type k over-represents to the probability for the phenotype to be present. Its sign represents the direction of the change: a positive value means an increase in probability, and a negative value means a decrease in probability. Its absolute value represents the strength of the effect. In SCIPAC, we call \(\Lambda _k\) the association strength of cell type k and the phenotype.

Note that this derivation does not involve likelihood, although the computation of \(\varvec{\beta }\) does. Here, it serves more as a definitional approach.

Definition of the association strength for other types of phenotype

Our definition of \(\Lambda _k\) relies on vector \(\varvec{\beta }\) . In the case of a binary phenotype, \(\varvec{\beta }\) are the coefficients of a logistic regression that describes a linear relationship between the expression profile and the log odds of having the phenotype, as shown in Eq. 3 . For other types of phenotype, \(\varvec{\beta }\) can be defined/computed similarly.

For a quantitative (i.e., continuous) phenotype, an ordinary linear regression can be used, and the left-hand side of Eq. 3 is changed to the quantitative value of the phenotype.

For a survival phenotype, a Cox proportional hazards model can be used, and the left-hand side of Eq. 3 is changed to the log hazard ratio.

For an ordinal phenotype, we use a proportional odds model

where \(j \in \{1, 2, ..., (J - 1)\}\) and J is the number of ordinal levels. It should be noted that here we use the right-tail probability \(\Pr (Y_{i} \ge j + 1 | X)\) instead of the commonly used cumulative probability (left-tail probability) \(\Pr (Y_{i} \le j | X)\) . Such a change makes the interpretation consistent with other types of phenotypes: in our model, a larger value on the right-hand side indicates a larger chance for \(Y_{i}\) to have a higher level, which in turn guarantees that the sign of the association strength defined according to this \(\varvec{\beta }\) has the usual meaning: a positive \(\Lambda _k\) value means a positive association with the phenotype-using the cancer stage as an example. A positive \(\Lambda _k\) means the over-representation of cell type k increases the chance of a higher cancer stage. In contrast, using the commonly used cumulative probability leads to a counter-intuitive, reversed interpretation.

Computation of the association strength in practice

In practice, \(\varvec{\beta }\) in Eq. 3 needs to be learned from the bulk data. By default, SCIPAC uses the elastic net, a popular and powerful penalized regression method:

In this model, \(l(\beta _{0}, \varvec{\beta })\) is a log-likelihood of the linear model (i.e., logistic regression for a binary phenotype, ordinary linear regression for a quantitative phenotype, Cox proportional odds model for a survival phenotype, and proportional odds model for an ordinal phenotype). \(\alpha\) is a number between 0 and 1, denoting a combination of \(\ell _1\) and \(\ell _2\) penalties, and \(\lambda\) is the penalty strength. SCIPAC fixes \(\alpha\) to be 0.4 (see Additional file 1 for discussions on this choice) and uses 10-fold cross-validation to decide \(\lambda\) automatically. This way, they do not become hyperparameters.

In SCIPAC, the fitting and cross-validation of the elastic net are done by calling the ordinalNet [ 87 ] R package for the ordinal phenotype and by calling the glmnet R package [ 88 , 89 , 90 , 91 ] for other types of phenotypes.

The computation of the association strength, as defined by Eq. 7 , does not only require \(\varvec{\beta }\) , but also \(\varvec{g}_k\) and \(\bar{\varvec{G}}\) . \(\bar{\varvec{G}}\) is simply the average expression profile of all bulk samples. On the other hand, \(\varvec{g}_k\) requires knowing the cell type of each cell. By default, SCIPAC does not assume this information to be given, and it uses the Louvain clustering implemented in the Seurat [ 24 , 25 ] R package to infer it. This clustering algorithm has one tuning parameter called “resolution.” SCIPAC sets its default value as 2.0, and the user can use other values. With the inferred or given cell types, \(\varvec{g}_k\) is computed as the centroid (i.e., the mean expression profile) of cells in cluster k .

Given \(\varvec{\beta }\) , \(\bar{\varvec{G}}\) , and \(\varvec{g}_k\) , the association strength can be computed using Eq. 7 . Knowing the association strength for each cell type and the cell-type label for each cell, we also know the association strength for every single cell. In practice, we standardize the association strengths for all cells. That is, we compute the mean and standard deviation of the association strengths of all cells and use them to centralize and scale the association strength, respectively. We have found such standardization makes SCIPAC more robust to the possible unbalance in sample size of bulk data in different phenotype groups.

Computation of the p -value

SCIPAC uses non-parametric bootstrap [ 92 ] to compute the standard deviation and hence the p -value of the association. Fifty bootstrap samples, which are believed to be enough to compute the standard error of most statistics [ 93 ], are generated for the bulk expression data, and each is used to compute (standardized) \(\Lambda\) values for all the cells. For cell i , let its original \(\Lambda\) values be \(\Lambda _i\) , and the bootstrapped values be \(\Lambda _i^{(1)}, \ldots , \Lambda _i^{(50)}\) . A z -score is then computed using

and then the p -value is computed according to the cumulative distribution function of the standard Gaussian distribution. See Additional file 1 for more discussions on the calculation of p -value.

Availability of data and materials

The simulated datasets [ 94 ] under three schemes are available at Zenodo with DOI 10.5281/zenodo.11013320 [ 95 ]. The SCIPAC package is available at GitHub website https://github.com/RavenGan/SCIPAC under the MIT license [ 96 ]. The source code of SCIPAC is also deposited at Zenodo with DOI 10.5281/zenodo.11013696 [ 97 ]. A vignette of the R package is available on the GitHub page and in the Additional file 2. The prostate cancer scRNA-seq data is obtained from the Prostate Cell Atlas https://www.prostatecellatlas.org [ 29 ]; the scRNA-seq data for the breast cancer are from the Gene Expression Omnibus (GEO) under accession number GSE176078 [ 34 , 98 ]; the scRNA-seq data for the lung cancer are from E-MTAB-6149 [ 99 ] and E-MTAB-6653 [ 71 , 100 ]; the scRNA-seq data for facioscapulohumeral muscular dystrophy data are from the GEO under accession number GSE122873 [ 101 ]. The bulk RNA-seq data are obtained from the TCGA database via TCGAbiolinks (ver. 2.25.2) R package [ 102 ]. More details about the simulated and real scRNA-seq and bulk RNA-seq data can be found in the Additional file 1.

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Veronique van den Berghe was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

This work is supported by the National Institutes of Health (R01CA280097 to X.L. and J.L, R01CA252878 to J.L.) and the DOD BCRP Breakthrough Award, Level 2 (W81XWH2110432 to J.L.).

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Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA

Dailin Gan & Jun Li

Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA

Yini Zhu & Xin Lu

Tumor Microenvironment and Metastasis Program, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, 46202, IN, USA

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Contributions

J.L. conceived and supervised the study. J.L. and D.G. proposed the methods. D.G. implemented the methods and analyzed the data. D.G. and J.L. drafted the paper. D.G., Y.Z., X.L., and J.L. interpreted the results and revised the paper.

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Correspondence to Jun Li .

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Supplementary information

Additional file 1. supplementary materials that include additional results and plots., additional file 2. a vignette of the scipac package., additional file 3. review history., rights and permissions.

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Gan, D., Zhu, Y., Lu, X. et al. SCIPAC: quantitative estimation of cell-phenotype associations. Genome Biol 25 , 119 (2024). https://doi.org/10.1186/s13059-024-03263-1

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Received : 30 January 2023

Accepted : 30 April 2024

Published : 13 May 2024

DOI : https://doi.org/10.1186/s13059-024-03263-1

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