BulkRNAseqAnalysis_from_countMatrix

This repository centers around an R program to perform standard bioinformatic analysis steps for RNA-seq raw count matrices and ultimately compile the results to a html or pdf report. Analysis steps include: Adding biomaRt annotation, filtering and QC, normalization, visual overview (PCA, heatmaps), k-means clustering + ORA (overrepresentation analysis), differential expression testing via DESeq2 + subsequent enrichment testing via GSEA (gene set enrichment analysis), and finally dataframe export as text file as well as the generation of an html or pdf report. An example analysis report is available as the html file "ExampleAnalysisReport_Demo.html" (or alternatively as pdf file "ExampleAnalysisReport_Demo.pdf" - however, without the interactive plots), which is obtained by running the script on a demo dataset (see below). Here are a few snapshots:

Contents

• BulkRNAseqAnalysis_from_CountMatrix.Rmd : R markdown script that performs the analysis based on specified input parameters (see code section "Chosen Parameters" in the Rmd file). Knit to html to obtain an html report of your analysis. The current parameter configuration is set to fully support the demo dataset (see below).

• functions.R : Functions required for the script (sourced automatically if located in the same folder).

• DemoDataset.txt : A raw count matrix that serves as the demo dataset so that anyone can explore the script. The parameters in BulkRNAseqAnalysis_from_CountMatrix.Rmd are already set to support the entire analysis of this dataset.

• ExampleAnalysisReport_Demo.html : The analysis report that is generated when running the script on the demo dataset. Check it out!

• ExampleAnalysisReport_Demo.pdf : The exact same analysis report but as pdf file, and therefore without the interactive plots.

Required Data Input

• A tab-separated raw read count matrix with rows corresponding to individual genes. The table is required to contain the following columns: a) a separate column of read counts for each unique sample, and b) a column containing Ensembl gene IDs.

Data Output

• An interactive html report created when knitting the BulkRNAseqAnalysis_from_CountMatrix.Rmd file to html file (see "ExampleAnalysisReport_Demo.html" or "ExampleAnalysisReport_Demo.pdf" as an example).
• Various figures and output tables that are generated when running the script. For more info, refer to the html report.

Things to Note:

• Please note that for the sake of automatization, this workflow only allows for simple contrasts via DESeq2. In other words, the workflow supports only simple pairwise group comparisons (i.e., H0: group A average - group B average = 0) while accounting for batch effects. For more complex contrasts with multiple factors and interaction effects, the code needs to be modified accordingly.

Acknowledgements

• The demo dataset was kindly provided by Lisa Sandner and Nicole Boucheron from the Institute of Immunology (IFI), Medical University of Vienna. The data is from a bulk RNAseq experiment of Rinl KO vs WT in naive and effector CD4+ T-cells, and has been published as part of a larger project (PMID: 37703004).

Session Info

R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.7.10

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Vienna
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] enrichplot_1.22.0           viridis_0.6.4               viridisLite_0.4.2           clusterProfiler_4.10.0     
 [5] msigdbr_7.5.1               cowplot_1.1.1               ggrepel_0.9.4               ashr_2.2-63                
 [9] gplots_3.1.3                reshape2_1.4.4              plotly_4.10.4               rlist_0.4.6.2              
[13] gprofiler2_0.2.2            dendextend_1.17.1           RColorBrewer_1.1-3          lubridate_1.9.3            
[17] forcats_1.0.0               stringr_1.5.0               dplyr_1.1.4                 purrr_1.0.2                
[21] readr_2.1.4                 tidyr_1.3.0                 tibble_3.2.1                ggplot2_3.5.0              
[25] tidyverse_2.0.0             DESeq2_1.42.0               SummarizedExperiment_1.32.0 Biobase_2.62.0             
[29] MatrixGenerics_1.14.0       matrixStats_1.1.0           GenomicRanges_1.54.1        GenomeInfoDb_1.38.0        
[33] IRanges_2.36.0              S4Vectors_0.40.1            BiocGenerics_0.48.1         biomaRt_2.58.0      

Used Libraries and other Resources

• DESeq2: Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

• gProfiler: Reimand J. g:Profiler--a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. (2007).

• msigdbr: Liberzon, A. et al. The Molecular Signatures Database Hallmark Gene Set Collection. Cell Syst. 1, 417–425 (2015).

• biomaRt: Durinck S, Spellman P, Birney E, Huber W . Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nature Protocols, 4, 1184–1191 (2009).

• clusterProfiler: Yu, G., Wang, L. G., Han, Y. & He, Q. Y. ClusterProfiler: An R package for comparing biological themes among gene clusters. Omi. A J. Integr. Biol. 16, 284–287 (2012).

• ggplot2: Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York (2016).

Used Libraries and other Resources