Answers.Body: <p>There is no question that <strong>lncDIFF</strong> is the approach to use, especially if the alternative is performing <strong>edgeR</strong> directly, and thats demonstrable (below). I don't think anyone would question the legitimacy of your approach. If you look at Fig 1 for the paper describing the test, <a href="https://media.springernature.com/full/springer-static/image/art%3A10.1186%2Fs12864-019-5926-4/MediaObjects/12864_2019_5926_Fig1_HTML.png?as=webp" rel="nofollow noreferrer">here</a> there is no question about it <strong>edgeR</strong> used directly does not perform for this task at all. The paper describing lncDIFF is here,</p>
  <p><a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5926-4" rel="nofollow noreferrer">https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5926-4</a></p>
  <p>You present <strong>lncDIFF</strong> as an option within <strong>edgeR</strong>. This I don't know, but <strong>lncDIFF</strong>  can be directly downloaded from GitHub, <a href="https://github.com/qianli10000/lncDIFF" rel="nofollow noreferrer">https://github.com/qianli10000/lncDIFF</a>.</p>
  <hr />
  <p><em>The one thing you have to be careful about is you are using plants, whilst these tests are designed around humans, see below for details.</em></p>
  <hr />
  <p>How it's tested: GLM methods versus an Exact test, e.g. Fisher's. Both are all valid tests. However, GLM methods are preferred  over an exact test, in my statistical experience.</p>
  <p>First, you need to obtain a <code>fit</code> before you can perform a test, which ever GLM you choose.</p>
  <pre><code>x &lt;- model.matrix(...)
  y &lt;- estimateDisp(...)
  fit &lt;- glmFit(y, x, robust=TRUE)
  lrt &lt;- glmLRT(fit, contrast=c(-1,1,0,0,0)) 
  </code></pre>
  <p>The basic issue is GLM likelihood ratio test versus quasi likelihood test, which the right approach? The clear answer is QLT because the authors of <strong>lncDIFF</strong> developed the test around QLT (see the paper). That shouldn't be general advice outside <strong>lncDIFF</strong></p>
  <p>The correct approach is to try both, if there major differences thats an issue: I would say in that scenario is consider  QLT - but only for <strong>lncDIFF</strong>, as described. If there are no major differences you are good to go.</p>
  
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Answers.Text: Thanks. I read the paper.  That answers the first part of my question. What about the last part? Also, among the various DE test methods in `edgeR` such as `exactTest()`, `glmFit()` and `glmLRT()`, `glmQLFit()` and `glmQLFTest()`, which is/are the one(s) most suitable for running DE tests on the other non-coding features?
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Answers.Text: Hi @ArkajyotiBanerjee upvotes and/or "accepted answers" are much more preferred to thanks: you can upvote. My advice is to open (a) separate question(s) for the remaining issues because this would clarify what is being sought. Multi-part questions are generally discouraged.
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Body: <p>I am currently working on DE analysis of coding as well as non-coding features of <em>A. thaliana</em> using the <code>edgeR</code> package.</p>
  <p>Is the negative binomial method that is normally used for the DE analysis of genes suitable for non-coding features such as miRNA, lncRNA, snRNA, snoRNA, pseudogenes, transposons, uORFs and 5' and 3' UTRs? Or are there special statistical methods that are more suitable for modelling each of these, like I have read that the zero-inflated model used by <code>lncDIFF</code> can better model lncRNA counts? I see that my miRNA counts matrix is abundant in zero counts and very low counts of genes. Should I use lncDIFF for that?</p>
  <p>Also, among the various DE test methods in edgeR such as <code>exactTest()</code>, <code>glmFit()</code> and <code>glmLRT()</code>, <code>glmQLFit()</code> and <code>glmQLFTest()</code>, which is/are the one(s) most suitable for running DE tests on such non-coding features?</p>
  
Tags: <rna-seq><differential-expression><edger><non-coding>
Title: Statistical methods suitable for DE analysis of non coding RNAs
ThreadText: # Statistical methods suitable for DE analysis of non coding RNAs
  
  (Asked by: username_0 on 2023-07-04)
  
  I am currently working on DE analysis of coding as well as non-coding features of *A. thaliana* using the `edgeR` package.
  
  Is the negative binomial method that is normally used for the DE analysis of genes suitable for non-coding features such as miRNA, lncRNA, snRNA, snoRNA, pseudogenes, transposons, uORFs and 5' and 3' UTRs? Or are there special statistical methods that are more suitable for modelling each of these, like I have read that the zero-inflated model used by `lncDIFF` can better model lncRNA counts? I see that my miRNA counts matrix is abundant in zero counts and very low counts of genes. Should I use lncDIFF for that?
  
  Also, among the various DE test methods in edgeR such as `exactTest()`, `glmFit()` and `glmLRT()`, `glmQLFit()` and `glmQLFTest()`, which is/are the one(s) most suitable for running DE tests on such non-coding features?
  
  **Tags:** rna-seq, differential-expression, edger, non-coding
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-07-05. Score: 1 | Accepted answer
  
  There is no question that **lncDIFF** is the approach to use, especially if the alternative is performing **edgeR** directly, and thats demonstrable (below). I don't think anyone would question the legitimacy of your approach. If you look at Fig 1 for the paper describing the test, [here](https://media.springernature.com/full/springer-static/image/art%3A10.1186%2Fs12864-019-5926-4/MediaObjects/12864_2019_5926_Fig1_HTML.png?as=webp) there is no question about it **edgeR** used directly does not perform for this task at all. The paper describing lncDIFF is here,
  
  <https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5926-4>
  
  You present **lncDIFF** as an option within **edgeR**. This I don't know, but **lncDIFF** can be directly downloaded from GitHub, <https://github.com/qianli10000/lncDIFF>.
  
  ---
  
  *The one thing you have to be careful about is you are using plants, whilst these tests are designed around humans, see below for details.*
  
  ---
  
  How it's tested: GLM methods versus an Exact test, e.g. Fisher's. Both are all valid tests. However, GLM methods are preferred over an exact test, in my statistical experience.
  
  First, you need to obtain a `fit` before you can perform a test, which ever GLM you choose.
  
  ```
  x <- model.matrix(...)
  y <- estimateDisp(...)
  fit <- glmFit(y, x, robust=TRUE)
  lrt <- glmLRT(fit, contrast=c(-1,1,0,0,0)) 
  
  ```
  
  The basic issue is GLM likelihood ratio test versus quasi likelihood test, which the right approach? The clear answer is QLT because the authors of **lncDIFF** developed the test around QLT (see the paper). That shouldn't be general advice outside **lncDIFF**
  
  The correct approach is to try both, if there major differences thats an issue: I would say in that scenario is consider QLT - but only for **lncDIFF**, as described. If there are no major differences you are good to go.
  
  > **Comments:**
  > **username_0**: Thanks. I read the paper.  That answers the first part of my question. What about the last part? Also, among the various DE test methods in `edgeR` such as `exactTest()`, `glmFit()` and `glmLRT()`, `glmQLFit()` and `glmQLFTest()`, which is/are the one(s) most suitable for running DE tests on the other non-coding features?  
  > **username_1**: Hi @username_0 upvotes and/or "accepted answers" are much more preferred to thanks: you can upvote. My advice is to open (a) separate question(s) for the remaining issues because this would clarify what is being sought. Multi-part questions are generally discouraged.  
  
  ---
  
  