Guideline for RNAseq downstream analysis

• Set the working directory to this file level. We recommend to use Projects in Rstudio.
• Use install_dependencies.R to install all packages used in these reports.

Run data with nf-core rnaseq

This templates assume data has been processed by nf-core/rnaseq. We recommend to use the samplesheet.csv used with nf-core as metadata file, where other relevant columns can be there even if they are not used by the pipeline.

Downstream analysis

• Modify information.R with the right information. You can use this file with any other Rmd to include the project/analysis information.
• Modify the params_*R that goes together with the Rmd templates with the right input files.
• params*example.R are parameters pointing to test data to be used as an example to test the reports.
• run_markdown.R is an example of code to run the Rmd with different parameters.

Quality assessment

01_quality_assessment/QC.Rmd is a template report that needs params_qc.R for nf-core/rnaseq outputs.

Follow instruction in the R and Rmd scripts to render it.

Differential expression

02_differential_expression/DEG.Rmd is a template for comparison between two groups. params_de.R has the information for the input files to load. You need to point to nf-core/rnaseq output files.

On the YAML header file of the Rmd you can specify some parameters or just set them up in the first chunk of code of the template. This template has examples of:

• sub-setting data
• two groups comparison
• volcano plot
• MA plot
• Pathway analysis: Over-Representation Analysis and Gene-Set-Enrichment Analysis
• Tables

Comparative analysis

• 03_comparative/Pair-wise-comparison-analysis.Rmd shows an exmaple on how to compare two differential expression analysis from the DEG.Rmd template.
• 03_comparative/Intersections.Rmd shows an example on how to compare multiple differential expression analyses from DEG.Rmd and find intersections.

Functional analysis

• 03_functional/GSVA.Rmd shows an example on how to use GSVA package for estimating variation of gene set enrichment through the samples of a expression data set
• 03_functional/Nonmodel_Organism_Pathway_Analysis.Rmd shows an example on how to run Gene Ontology over-representation, KEGG over-representation, and KEGG gene set enrichment analysis (GSEA) for non-model organisms using data from Uniprot. params_nonmodel_org_pathways.R has the information for the input files to load.
• 03_functional/Immune-deconvolution.Rmd shows an example on how to run immune cell type deconvolution. params_immune_deconv.R has the information for the input files to load.

Gene pattern analysis

• 04_gene_patterns/WGCNA.Rmd shows an example on how to use the WGCNA package to find gene modules in the gene expression data.
• 04_gene_patterns/DEGpatterns.Rmd shows an example on hot to cluster set of genes across conditions and time points to identify specific profiles.