Answers.Body: <p>You can take a look at the recently published article: <a href="https://www.nature.com/articles/nmeth.4612" rel="noreferrer">Bias, robustness and scalability in single-cell differential expression analysis</a>.</p>
  
  <blockquote>
    <p>We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that <strong>bulk RNA-seq analysis methods do not generally perform worse</strong> than those developed specifically for scRNA-seq. </p>
  </blockquote>
  
  <p>Then you can decide which method fits better to your type of scRNA-seq protocol. Since Seurat offers DESeq2 could be a good idea to use it, but this depends a lot on your data. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-11T10:07:40.193Z
Answers.LastActivityDate: 2018-05-11T10:07:40.193Z
Answers.UserDisplayName: jxx_fa
Body: <p>In <code>Seurat</code> we can specify <a href="https://satijalab.org/seurat/de_vignette.html" rel="nofollow noreferrer">multiple methods</a> for finding <code>DE</code> genes.</p>
  
  <p>I am wondering when should we use which one, the use cases. As I understand, whole <code>Seurat</code> package is used for single cell sequencing analysis. I ran different methods for my dataset that was produced from brain cells, around <code>800</code> cells in each sample, by single cell sequencing. Now I am confused which methods are the most appropriate to use, which results are the most reliable. For single cell sequencing when would you prefer to use one method over the other?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-05-11T07:01:33.613Z
Comments.Text: what methods did you try (all of them?)? Did you compare between them? Did you review the mathematical background and assumptions they make to see if they fit your data?
Comments.UserDisplayName: llrs
Tags: <r><single-cell><seurat>
Title: Which are the use cases for the methods for DE in Seurat
ThreadText: # Which are the use cases for the methods for DE in Seurat
  
  (Asked by: username_0 on 2018-05-11)
  
  In `Seurat` we can specify [multiple methods](https://satijalab.org/seurat/de_vignette.html) for finding `DE` genes.
  
  I am wondering when should we use which one, the use cases. As I understand, whole `Seurat` package is used for single cell sequencing analysis. I ran different methods for my dataset that was produced from brain cells, around `800` cells in each sample, by single cell sequencing. Now I am confused which methods are the most appropriate to use, which results are the most reliable. For single cell sequencing when would you prefer to use one method over the other?
  
  **Tags:** r, single-cell, seurat
  
  > **Comments:**
  > **username_1**: what methods did you try (all of them?)? Did you compare between them? Did you review the mathematical background and assumptions they make to see if they fit your data?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2018-05-11. Score: 5 | Accepted answer
  
  You can take a look at the recently published article: [Bias, robustness and scalability in single-cell differential expression analysis](https://www.nature.com/articles/nmeth.4612).
  
  > We evaluated 36 approaches using experimental and synthetic data and found considerable differences in the number and characteristics of the genes that are called differentially expressed. Prefiltering of lowly expressed genes has important effects, particularly for some of the methods developed for bulk RNA-seq data analysis. However, we found that **bulk RNA-seq analysis methods do not generally perform worse** than those developed specifically for scRNA-seq.
  
  Then you can decide which method fits better to your type of scRNA-seq protocol. Since Seurat offers DESeq2 could be a good idea to use it, but this depends a lot on your data.
  
  
  ---
  
  