README

This repository holds a nextflow pipeline for analysing gene expression studies. The the pipeline allows for experimental design with multiple doses (0, 10, 100, 1000, etc). The pipeline expect quantification files (quant.sf) with a transcript-to-gene index file (tx2gene.tsv), both generated with Salmon, and sample metadata file as input. The pipeline outputs tables of (1) differentially expressed genes and (2) gene ontology analysis results which are combined to a report generated with Quarto. The pipeline also outputs the tables as excel files to be included as supplementary tables in a scientic report.

Important

This pipeline is optimised for SLURM on a high-performance computing (HPC) cluster (UPPMAX).

Quantification files

To generate methylation coverage files from sequencing files refer to nf-core/rnaseq pipeline

Differential expression

Differential expression was analysed with the R-package DESeq2.

Gene ontology analysis

To investigate if any biological functions, processes or pathways are enriched (over-represented) the Over Representation Analysis (ORA) Boyle et al., 2004 method is used. ORA uses hypergeometric distribution and compares the differentially methylated genes with all genes in the dataset. The p-values are adjusted to q-values for multiple corretion (significance threshold qvalue < 0.2).

Enrichment is analysed in three databases; (1) Gene Ontology (GO), (2) Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathways. GO and KEGG enrichment are tested with the R-package clusterProfiler, Yu et al., 2012, Wu et al., 2021. The reactome pathways are tested with the R-package ReactomePA, Yu et al., 2016.

Reproducibility

Run the pipeline

To start the pipeline, wrap the code below into a shell script. Make sure to change the paths to the correct files/folders.

NXF_HOME=".nextflow/"
PROJECT='slurm-projID'
QUANT_FILES='path/to/quant_files'
TX2GENE_FILE='path/to/tx2gene.tsv'
SAMPLE_INFO_FILE='path/to/sample_info'
SPECIES='mouse/human'
ENSEMBL_VERSION=115

nextflow pull andreyhgl/transcriptome-analysis

nextflow run andreyhgl/transcriptome-analysis -r main \
  -profile uppmax \
  --project "$PROJECT" \
  --diff_analysis_package 'DESeq2' \
  --quant_files "$QUANT_FILES" \
  --tx2gene "$TX2GENE_FILE" \
  --sample_info "$SAMPLE_INFO_FILE" \
  --species "$SPECIES" \
  --ensembl_version "$ENSEMBL_VERSION"

Containers

For reproducibility this pipeline uses two singularity containers, which can be downloaded from the Cloud Library. The RNAseq container holds most of the R-packages used in the analysis, while gene-ontology container holds gene ontology related R-packages

# apptainer/singulartiy also works

IMAGE1='library://andreyhgl/singularity-r/rnaseq'
IMAGE2='library://andreyhgl/singularity-r/gene-ontology'

apptainer pull "$IMAGE1"
apptainer pull "$IMAGE2"

To run scripts manually with the containers use the exec flag or run the script interactively with shell.

# execute script
apptainer exec "$IMAGE" <scriptfile>

# run script interactively
apptainer shell "$IMAGE"
$ Rscript <scriptfile>

The pipeline

The nextflow pipeline produce the following:

• Ensembl database table containing gene annotations
• Quality control plots: PCA, distance plots
• Differentially expressed genes table
• Gene ontology analysis
• Supplementary files (plots, excel-tables)
• Concatinated tables (for easy import for results report)
• A final report that summaries the results

Preparation

Metadata

Setup the metadata.csv with each row representing a sample in the column order: sample id and treatment/dose:

id,dose
sample1,0
sample2,0
sample3,10
sample4,100
sample5,10
sample6,100

etc....