Answers.Body: <p>Partially answered before. I am assuming this is a <strong>lncDIFF</strong> calculation. For the <strong>lncDIFF</strong> paper it is unlikely to work as the following figure displays,</p>
  <p><a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5926-4/figures/4" rel="nofollow noreferrer">https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5926-4/figures/4</a></p>
  <p>However, don't take my word for this, test it. You should test the option explicitly via <code>glmQLFTest</code> and <code>glmLRT</code>. If you perform either of these tests with or without the filter the better fit (one not rejected) is the correct answer. I strongly predict the filtered data will be rejected and unfiltered data  (hopefully) will pass.</p>
  <p>The question of what is better <code>glmQLFTest</code> versus <code>glmLRT</code> is discussed in the previous answer, here <a href="https://bioinformatics.stackexchange.com/questions/21259/statistical-methods-suitable-for-de-analysis-of-non-coding-rnas">Statistical methods suitable for DE analysis of non coding RNAs</a></p>
  <p>Just keep in mind this is plant data and these tests are developed for humans.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-07-05T15:32:02.980Z
Answers.LastActivityDate: 2023-07-05T17:02:37.490Z
Answers.LastEditDate: 2023-07-05T17:02:37.490Z
Answers.UserDisplayName: M__
Body: <p>I am trying to do DE analysis of non-coding features of <em>A. thaliana</em>. I find in the miRNA and lncRNA counts file that they are abundant in zero counts, and most of the non-zero counts are very low. Now, for the DE analysis of coding features, an inbuilt filtering method to remove the extremely low counts is available in edgeR, called <code>filterByExpr()</code>. Is this function suitable for only those features with high counts like coding sequences, or is it suitable for non-coding features with low counts and low library sizes too?
  If not, then what kind of filtering criteria should I use for filtering out genes with insignificant counts? Or should I filter out only the ones with row sums 0?</p>
  
Tags: <rna-seq><differential-expression><filtering><edger><non-coding>
Title: Filtering criteria for non-coding features with very low counts
ThreadText: # Filtering criteria for non-coding features with very low counts
  
  (Asked by: username_0 on 2023-07-04)
  
  I am trying to do DE analysis of non-coding features of *A. thaliana*. I find in the miRNA and lncRNA counts file that they are abundant in zero counts, and most of the non-zero counts are very low. Now, for the DE analysis of coding features, an inbuilt filtering method to remove the extremely low counts is available in edgeR, called `filterByExpr()`. Is this function suitable for only those features with high counts like coding sequences, or is it suitable for non-coding features with low counts and low library sizes too?
  If not, then what kind of filtering criteria should I use for filtering out genes with insignificant counts? Or should I filter out only the ones with row sums 0?
  
  **Tags:** rna-seq, differential-expression, filtering, edger, non-coding
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-07-05. Score: 0
  
  Partially answered before. I am assuming this is a **lncDIFF** calculation. For the **lncDIFF** paper it is unlikely to work as the following figure displays,
  
  <https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5926-4/figures/4>
  
  However, don't take my word for this, test it. You should test the option explicitly via `glmQLFTest` and `glmLRT`. If you perform either of these tests with or without the filter the better fit (one not rejected) is the correct answer. I strongly predict the filtered data will be rejected and unfiltered data (hopefully) will pass.
  
  The question of what is better `glmQLFTest` versus `glmLRT` is discussed in the previous answer, here [Statistical methods suitable for DE analysis of non coding RNAs](https://bioinformatics.stackexchange.com/questions/21259/statistical-methods-suitable-for-de-analysis-of-non-coding-rnas)
  
  Just keep in mind this is plant data and these tests are developed for humans.
  
  
  ---
  
  