Analysis pipeline, from quant-files => calculation of differential gene expression => gene annotation analysis => pathway analysis => report.
The pipeline comes with some default parameters and will look inside folders data/, for metadata files and results/ for quantification files, assuming the alignment and quantification has been performed with nf-core/rnaseq pipeline.
project/
├── assets/quarto_report/ # Quarto files contained within assets/
│ ├── _quarto.yml
│ ├── styles.css
│ └── report.qmd
│
├── conf/ # pipeline configs
│ ├── base.config
│ └── modules.config
├── bin/ # Mostly R-scripts
├── modules/
├── subworkflows/local/
│ ├── deseq2/
│ ├── gene_annotation/
│ └── pathway_enrichment/
├── main.nf
├── nextflow.config
│
├── data/ # metadata for the project
├── results/ # nf-core/rnaseq output
│
└── README.mdThe pipeline requires a metadata file with columns: sample. Pipeline defaults to data/metadata.csv, but can be manually set: --metadata 'path/to/metadata.csv'.
The DESeq2 formula design is decided by a column name and is passed with --deseq2_design 'column-name'.
nextflow run andreyhgl/transcriptome-analysis \
--metadata 'path/to/metadata.csv' \
--deseq2_design 'column-name'Defaults to --quant_dir results/star_salmon
nextflow run andreyhgl/transcriptome-analysis \
--metadata 'path/to/metadata.csv' \
--quant_dir 'path/to/quant_files' \
--deseq2_design 'column-name' Categorises genes according to their genomic positions and overlapping regulatory regions; transposable elements, enhancers, imprinted genes.
Over-representation analysis. Comparison of genesets from gene ontology, Reactome, KEGG. (clusterProfiler, ReactomePA).
Interactive quarto results report,
To start the pipeline:
nextflow run andreyhgl/transcriptome-analysis \
-profile apptainer \
-params-file params.yml \
-resume \
-bgAll parameters comes with defaults, set inside nextflow.config
params {
// Input
metadata = 'data/metadata.csv'
tx2gene = 'results/star_salmon/salmon.merged.tx2gene.tsv'
quant_dir = 'results/star_salmon'
ensembl_version = 115
species = 'human'
// DESeq2
deseq2_design = 'group'
deseq2_min_count = 10
deseq2_max_zero_frac = 0.5
deseq2_alpha = 0.05
deseq2_lfc_threshold = 0The pipeline uses docker containers:
- differential gene expression
- gene annotations
- pathway analysis
- quarto report