Eoulsan – DESeq2 module

Last Published: 2022-12-13 14:46 | Version: 2.6.1

GenomiqueENS genomics core facility | École normale supérieure

About Eoulsan

Getting Eoulsan

Documentation

Quickstart Guide
Design file v1
Design file v2
Workflow file
Configuration file
Built-in modules
- Create mapper index
- Create Minimap2 mapper index
- Create STAR mapper index
- FastQC
- Filter reads
- Map reads
- Filter alignments
- Filter and map
- Sam2Bam
- Bam2Sam
- Sam2Fastq
- Splice2Bed
- Expression
- Expression annotation
- MultiQC
- Normalization
- Differential analysis
- Differential analysis annotation
- Differential analysis with DESeq2
- Matrix to Cell Ranger Matrix
- R SingleCellExperiment creator
- Splitters and mergers
- Shell
- Import
Command line
Cluster configuration
Hadoop configuration
Data repositories configuration
Known bugs
Mailing list

Development

DESeq2 generalities

This module allows to perform the normalization and the differential expression analysis on expression step results. This module is based on the DESeq2 package of Bioconductor.

Name: DESeq2

Input port:
- input: expression files in TSV format (format: expression_results_tsv)

Output:
- Graphs for the normalization:
  - Unpooled sample clustering.
  - Unpooled PCA.
  - null counts barplot: percentage of genes with null counts per sample.
  - unpooled counts barplot: number of counts per sample.
  - unpooled counts boxplot.
  - pooled counts barplot: number of counts per sample (After RepTech pooling).
  - pooled counts boxplot.
  - pooled and normalized clustering.
  - pooled and normalized PCA.
  - pooled and normalized boxplot.
  - most expressed features plot: percentage of reads on the gene with the maximum number of reads.
- Graphs for the differential analysis:
  - dispersion plot.
  - p-value plot.
  - adjusted p-value plot.
  - MA plot.
- TSV files:
  - raw counts matrix.
  - pooled counts matrix.
  - normalized counts matrix.
  - diffana matrix: one for each comparison performed during the differential expression analysis.

Workflow parameters for DESeq2

Parameters :

Parameter	Type	Description	Default value
norm.fig	boolean	If true, generate the figures of the normalization.	true
diffana.fig	boolean	If true, generate the figures of the differential analysis.	true
norm.diffana	boolean	If set to false, skip the normalization and the differential analysis. This option is used to produce the contrast matrix without doing the normalization and the differential analysis.	true
diffana	boolean	If set to false, skip the differential analysis.	true
size.factors.type	string	Determination of the size factors type to use during the differential analysis (value: ratio or iterate).	ratio
fit.type	string	Determination of the fit type for the distance estimation to use during the differential analysis (value: parametric, local or mean).	parametric
statistical.test	string	Statistical test to be used during the differential analysis (value: Wald or LRT).	Wald
r.execution.mode	string	The R execution mode. The available mode values are: process, rserve and docker.	process
rserve.servername	string	The Rserve server name to use in rserve execution mode	not set
docker.image	string	The Docker image to use in Docker execution mode.	bioconductor/release_sequencing:3.1

Required R packages installation

Eoulsan differential expression analysis module use R with the package DESeq2 as statistical backend and FactoMineR. Differential analysis module was tested with R 3.2.0, DESeq2 1.8.1 and FactoMineR 1.28. You can install DESeq2 and FactoMineR R packages on your computer or on a Rserve server, or you can use the docker containing all the packages needed to run deseq2: bioconductor/release_sequencing:3.1 docker image. These packages are also present on the Eoulsan docker: Eoulsan docker.

How to use DESeq2?

DESeq2 can be used in 2 different modes: classic mode or contrast mode.

Classic mode

The classic mode performs the differential analysis on the "Condition" column of the design file. During the differential expression analysis each condition is compared to the others. The "Reference" column can be used to specify references for the comparison.

In the design file: To design an experiment with a classical mode differential expression analysis you can use the following example in the design file:
```
Exp.1.name=exp1
Exp.1.skip=false
Exp.1.reference=WT-day1
							
```
The keys "reference" is optional and can be replaced by a "Reference" column in the design file.

Contrast mode

The contrast mode performs the differential analysis using contrast vector.

In the design file: To design an experiment with a contrast mode differential analysis you can use the following example in the design file:
```
Exp.2.name=exp2
Exp.2.skip=false
Exp.2.contrast=true
Exp.2.buildContrast=true
Exp.2.model=~type+day+type:day
Exp.2.comparisons=WT1_vs_KO1:typeWT%dayday1_vs_typeKO%dayday1;\
WT2_vs_KO2:typeWT%dayday2_vs_typeKO%dayday2
							
```
- You need to set the options contrast and buildContrast at true.
- You need to set the DESeq2 model including the the column needed for the contrast.
- You need the comparisons options where you specify the comparison to be done according the contrasts. The contrast vectors will be generated from this option. See the Design file v2 section for more information on this option.