bulk-rnaseq

Simple workflow to quantify gene-level RNA abundance and detect differentially expressed genes (DEGs) from bulk RNAseq samples. The pipeline uses salmon to quantify transcript level abundance and DESeq2 to normalize counts and detect DEGs.

Resource Requirements

Salmon usually works with 8GB to 12GB of RAM. 16GB is recommended for DESeq2 with small datasets. Larger amounts of memory will be required for larger datasets.

Installation

1. Install Singularity and snakemake
2. Download the salmon references or build your own
3. Clone this repository or a create new repository from this template
4. Describe your samples in samples.csv
5. Modify the settings in config.yaml
6. (Optional) If you plan on using a SLURM cluster, fill out the #SBATCH directives in run_pipeline.sh and the account field in cluster.json.

Salmon vs Kallisto

As of April 2022, bulk-rnaseq only supports Salmon quantification. Older versions supported Kallisto, but this was dropped due to better Salmon integration with tximeta

Sample File Configuration

bulk-rnaseq requires you specify a CSV file with a metadata about samples to analyze. There are several required columns:

• patient
• condition
• fq1
• fq2

The patient and condition columns are used to create a sample specific ID for downstream processing and identification. The values specified in the these columns should create unique values when combined as {patient}-{condition}. condition should specify the experimental condition of the sample (normal or tumor, primary or metastatic etc).

NOTE salmon will automatically infer the library stranding and choose the best option.

Running the pipeline

Run jobs locally

snakemake -j [cores] --use-singularity

Run jobs on a SLURM cluster

# Must run within the bulk-rnaseq directory
sbatch run_pipeline.sh

Output

Results are stored in results/[gene|transcript]-level and include:

• normalized_counts.rds - DESeq2 object with VST normalized log2 counts. Useful for downstream visualization, clustering, and machine learning
• pca_plot.svg - Plot of the samples described by the first two principal components. Useful to check for batch effects and identify bad samples

Each contrast has its own folder in results/ with the following files:

• mle_foldchanges.csv - List of log2 fold changes estimated by DESeq2 using maximum likelihood estimation (MLE)
• map_foldchanges.csv - List of log2 fold changes estimated by DESeq2 via shrinkage using the apeglm package
• mle_ma.svg - MA plot showing the MLE fold changes
• map_ma.svg - MA plot showing the MAP fold changes

The full DESeq2 object used for differential expression analysis can be found in deseq2/.

A MultiQC html report with quantification statistics and FASTQC reports for each sample is located in qc/. Check this to verify a reasonable proportion of reads were pseudoaligned. validateFastq_summary.csv has the results of running qc/validateFastq on each sample's input FASTQ files. This checks for proper FASTQ formatting and properly sorted read headers.