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Analysis of differential gene expression in PRJNA630433 using DESeq2

DESeq2 Analysis

To begin, navigate to the history PRJNA630433 FeatureCounts Counting on HISAT2 bam alignments and copy the three dataset collections of counts generated by FeatureCounts: Dc FeatureCounts counts, Mo FeatureCounts counts and Oc FeatureCounts counts into a new history that you will name PRJNA630433 DESeq2 analysis

Then, search for DESeq2 in the tool search bar

DESeq2 settings

  • how

    → Select datasets per levels

  • 1: Factor

    → Tissue

  • 1: Factor level

    Note that there will be three factor levels in this analysis: Dc, Mo and Oc.

    → Oc

  • Counts file(s)

    → select the data collection icon, then 15: Oc FeatureCounts counts

  • 2: Factor level

    → Mo

  • Counts file(s)

    → select the data collection icon, then 10: Mo FeatureCounts counts

  • 3: Factor level (you must click on ➕ Insert Factor level)

    → Dc

  • Counts file(s)

    → select the data collection icon, then 5: Mo FeatureCounts counts

  • (Optional) provide a tabular file with additional batch factors to include in the model.

    → Leave to Nothing selected

  • Files have header?

    → Yes

  • Choice of Input data

    → Count data

  • Advanced options

    → No, leave folded

  • Output options

    → Unfold and check Output all levels vs all levels of primary factor (use when you have >2 levels for primary factor) in addition to the already checked Generate plots for visualizing the analysis results

    → Leave Alpha value for MA-plot to 0,1: note that this option is used for plots and does not impact DESeq2 results

  • Run Tool

Note on the order of Factors levels in the DESeq2 html form

As specified in the help section of the DESeq2 html form, the order of the Factors levels matters ! See why in that section.

In a nutshell, the Factor level you put as last in the form, will be taken as the reference Factor level.

Thus in our use case, the condition Mo will serve as reference condition for differential gene expression in the DESeq2 analysis.

Inspect DESeq2 plots

There is a lot of information here which we will discuss online or in live

Add a missing header to DESeq2 tabular outputs

If you have a look to three datasets in the collection DESeq2 result files on data 4, data 3, and others, you'll see that a header indicating what is the content of the 7 columns is missing. This lack of header is inconfortable when you are not very familiar with DE analyses.

Indeed, this header should be 

GeneID  Base_mean   log2FC  StdErr  Wald-Stats  P-value P-adj

Fortunately, there is a nice tool in Galaxy to quickly add this header.

Add Header settings

  • List of Column headers (comma delimited, e.g. C1,C2,...)

    → GeneID,Base_mean,log2FC,StdErr,Wald-Stats,P-value,P-adj

  • Data File (tab-delimted)

    → Select the data collection icon, then DESeq2 result files on data 4, data 3, and others

  • Run Tool

⚠ Rename the new collection DESeq2 Results Tables