RNA-seq Differential expression and filtering workflow

This workflow works only with an experimental setup containing exactly 2 conditions with at least 2 replicates per condition.

Inputs dataset

Counts from changed condition: Counts from experimental condition or changed condition. For eg. counts from treatment or knockdown samples.
Counts from reference condition: Counts from reference condition or base condition. For eg. counts from untreated or wildtype samples.
Gene Annotaton: The same GTF file used for mapping and quantification. It is used to annotate the DESeq2 results table. Ideally, the GTF file should contain gene_id, gene_biotype and gene_name attributes.

Count files have header: Indicate whether your input count files have a header line. Usually, count files generated from featureCounts tool have a header line whereas count files from RNA-STAR do not have.
Adjusted p-value threshold: All the genes with an adjusted p-value less than this value are considered as differentially expressed. With a value of 0.05, expect 5% of false positives in the differentially expressed genes list. If empty, a default value of 0.05 is used.
log2 fold change threshold: All the genes with an absolute fold change (regarless of up or down regulation) more than this value are selected. A log2 FC of 3 equals to an absolute fold change of 8 (2³). If empty, a default value of 1.0 is used.

The workflow uses DESeq2 for performing differential expression analysis. In addition to the results table, it also produces normalized counts table.
The results table is annotated with gene positions, biotypes, gene symbols.
The annotated results table is further filtered with the input adjusted p-value and log2 fold change thresholds.
A valcano plot is generated with top 10 significantly differentially expressed genes.
A heatmap of log trasformed normalized counts and another heatmap of Z-scores is generated.