Copy with Scaffolding XML of DESeq2 (multifactorial pairwise comparisons)

Currently DeSeq apps (both DeSeq and DeSeq2) in DE, do not allow multifactorial pairwise comparison of RNA-Seq data for differential gene expression analysis. The app - "DEseq2 (multifactorial pairwise comparisons)" is based on SARTools (R package dedicated to the differential analysis of RNA-seq data) which allows multifactorial pairwise comparison of RNA-Seq data for differential gene expression analysis. It provides tools to generate descriptive and diagnostic graphs, to run the differential analysis with the DEseq2 package, and to export the results into easily readable tab-delimited files. It also facilitates the generation of an HTML report which displays all the figures produced, explains the statistical methods, and gives the results of the differential analysis.

DESeq2 estimates differentially expressed gene lists based on a negative binomial distribution model. Previous methods for identifying differentially expressed gene lists assumed a Poisson distribution; however, Poisson does not account for variation (or overdispersion) found in expression data. DESeq2 uses a negative binomial distribution (similar to edgeR), assuming variance in the case of few replicates.
The input is a tab-delimited file containing genes and their expression values. The results include files detailing the results of differential expression testing (one that includes all of the results, and one that only includes the results that exceed a minimum false-discovery rate). Also included for visualization purposes are plots of the estimated dispersions, the log fold changes against the mean normalized counts and a histogram of p-values. The plots are purely for visualization purposes and may not be necessary for all users.

Prerequisites

1. A CyVerse account (Register for a CyVerse account at https://user.cyverse.org/).

2. An up-to-date Java-enabled web browser. (Firefox recommended. If you wish to work with your own large datasets and upload them using iCommands, Chrome is not suitable due to its issues in utilizing 64-bit Java.)

3. Input files:

Example of a Raw counts file:

Example of a count file per sample with two tab delimited columns without header. 

4. Parameters

This tutorial uses the test data that is stored in the Data Store at Community Data > iplantcollaborative > example_data > DESeq2_multi.          

Open the DE Apps window and search for edgeR (multficatorial pairwise comparisons).

In the Analysis Name:

1. Change the name for your analysis (optional).

2. Enter any comments (optional).

3. In the Select output folder field, click Browse and navigate to the folder of your choice. You can leave the default name iplant/home/username/analyses.

4. To retain copies of the input files in your analysis results output folder, click the Retain Inputs checkbox.

Click the Input files panel:

1. If you want to test the file_type test data:

   1. For the Target file , browse to select target3.txt inside file_type.

   2. For the Row counts file, browse to select counts3.txt.

2. If you want to test the folder_type test data:

   1. For the Target file , browse to select target3.txt inside file_type

   2. For the Raw counts folder, browse to select raw1 inside folder_type

3. Please note: Only one of the above two options need to be selected

Click on the Parameters panel:

1. Project name: test_deseq2_file_type

2. Author Name: Upendra

3. Reference biological condition: OP

4. batch:

5. Variable of Interest: group

6. FeaturesToRemove: alignment_not_unique,ambiguous,no_feature,not_aligned,too_low_aQual

7. locfunc: median

8. Transformation method for PCA/clustering: VST

9. Mean-variance relationship: parametric

10. Independent Filtering: TRUE

11. Cooks Cutoff: TRUE

12. Significance threshold: 0.05

13. p-value adjustment method: BH

14. colors: dodgerblue,orange,green

Click Launch Analysis.

The following files and figures are generated

• barplotTC.png: total number of reads per sample;

• barplotNull.png: percentage of null counts per sample;

• densplot.png: estimation of the density of the counts for each sample;

• majSeq.png: percentage of reads caught by the feature having the highest count in each sample;

• pairwiseScatter.png: pairwise scatter plot between each pair of samples and SERE values (not produced if more than 30 samples);

• diagSizeFactorsHist.png: diagnostic of the estimation of the size factors;

• diagSizeFactorsTC.png: plot of the size factors vs the total number of reads;

• countsBoxplot.png: boxplots on raw and normalized counts;

• cluster.png: hierachical clustering of the samples (based on VST or rlog data for DESeq2);

• PCA.png: first and second factorial planes of the PCA on the samples based on VST or rlog data;

• dispersionsPlot.png: graph of the estimations of the dispersions and diagnostic of log-linearity of the dispersions;

• rawpHist.png: histogram of the raw p-values for each comparison;

• MAplot.png: MA-plot for each comparison (log ratio of the means vs intensity);

• volcanoPlot.png: vulcano plot for each comparison ($-\log_{10}\text{(adjusted P value)}$ vs log ratio of the means).

Some tab-delimited files are exported in the tables directory. They store information on the features as $\log_2\text{(FC)}$ or p-values and can be read easily in a spreadsheet:

• TestVsRef.complete.txt: contains all the features studied;

• TestVsRef.down.txt: contains only significant down-regulated features, i.e. less expressed in Test than in Ref;

• TestVsRef.up.txt: contains only significant up-regulated features i.e. more expressed in Test than in Ref.

For more information of how to interpret these figures, files, troubleshooting and FAQ please refer here