Snakemake pipeline for quantitation of single-cell data using salmon alevin

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This is the template for a new Snakemake workflow. Replace this text with a comprehensive description covering the purpose and domain. Insert your code into the respective folders, i.e. scripts , rules , and envs . Define the entry point of the workflow in the Snakefile and the main configuration in the config.yaml file.

Authors

Kevin Rue-Albrecht (@kevinrue)

Usage

If you use this workflow in a paper, don't forget to give credits to the authors by citing the URL of this (original) repository and, if available, its DOI (see above).

Step 1: Obtain a copy of this workflow

Create a new github repository using this workflow as a template .
Clone the newly created repository to your local system, into the place where you want to perform the data analysis.

Step 2: Configure workflow

Configure the workflow according to your needs via editing the files in the config/ folder. Adjust config.yaml to configure the workflow execution, and samples.tsv to specify your sample setup.

Step 3: Install Snakemake

Install Snakemake using conda :

conda create -c bioconda -c conda-forge -n snakemake snakemake

For installation details, see the instructions in the Snakemake documentation .

Step 4: Execute workflow

Activate the conda environment:

conda activate snakemake

Test your configuration by performing a dry-run via

snakemake --use-conda -n

Execute the workflow locally via

snakemake --use-conda --cores $N

using $N cores or run it in a cluster environment via

snakemake --use-conda --cluster qsub --jobs 100

snakemake --use-conda --drmaa --jobs 100

If you not only want to fix the software stack but also the underlying OS, use

snakemake --use-conda --use-singularity

in combination with any of the modes above. See the Snakemake documentation for further details.

Step 5: Investigate results

After successful execution, you can create a self-contained interactive HTML report with all results via:

snakemake --report report.html

This report can, e.g., be forwarded to your collaborators. An example (using some trivial test data) can be seen here .

Step 6: Commit changes

Whenever you change something, don't forget to commit the changes back to your github copy of the repository:

git commit -a
git push

Step 7: Obtain updates from upstream

Whenever you want to synchronize your workflow copy with new developments from upstream, do the following.

Once, register the upstream repository in your local copy: git remote add -f upstream [email protected]:snakemake-workflows/snakemake_alevin_quant.git or git remote add -f upstream https://github.com/snakemake-workflows/snakemake_alevin_quant.git if you do not have setup ssh keys.
Update the upstream version: git fetch upstream .
Create a diff with the current version: git diff HEAD upstream/master workflow > upstream-changes.diff .
Investigate the changes: vim upstream-changes.diff .
Apply the modified diff via: git apply upstream-changes.diff .
Carefully check whether you need to update the config files: git diff HEAD upstream/master config . If so, do it manually, and only where necessary, since you would otherwise likely overwrite your settings and samples.

Step 8: Contribute back

In case you have also changed or added steps, please consider contributing them back to the original repository:

Fork the original repo to a personal or lab account.
Clone the fork to your local system, to a different place than where you ran your analysis.
Copy the modified files from your analysis to the clone of your fork, e.g., cp -r workflow path/to/fork . Make sure to not accidentally copy config file contents or sample sheets. Instead, manually update the example config files if necessary.
Commit and push your changes to your fork.
Create a pull request against the original repository.

Testing

Test cases are in the subfolder .test . They are automatically executed via continuous integration with Github Actions .

Code Snippets

shell:
    """
    {DATETIME} > {log.time} &&
    rm -rf results/alevin/{wildcards.sample} &&
    salmon alevin -l ISR -i {input.index} \
    -1 {input.fastq1} -2 {input.fastq2} \
    -o results/alevin/{wildcards.sample} -p {params.threads} --tgMap {input.tgmap} \
    --chromium --dumpFeatures \
    {params.cells_option} \
    2> {log.err} > {log.out} &&
    {DATETIME} >> {log.time}
    """

SnakeMake Salmon From line 23 of rules/alevin.smk

script:
    "../scripts/barcode_rank.R"

SnakeMake From line 10 of rules/qc.smk

message("Started")

#
# Manage script inputs
#

quants_mat_file <- snakemake@input[["quants"]]
plot_file <- snakemake@output[[1]]

#
# Manage R packages
#

library(tidyverse)
library(tximport)
library(DelayedMatrixStats)
library(cowplot)
library(sessioninfo)

#
# Main script
#

txi <- tximport::tximport(files = quants_mat_file, type = "alevin")

plot <- tibble(
    barcode = colnames(txi$counts),
    total = DelayedMatrixStats::colSums2(txi$counts)
) %>%
    arrange(desc(total)) %>%
    mutate(rank = row_number()) %>%
    ggplot() +
    geom_point(aes(rank, total)) +
    scale_y_log10() + scale_x_log10() +
    theme_cowplot()
ggsave(filename = plot_file, plot = plot)

#
# session info
#

sessioninfo::session_info()

message("Completed")