jianhong/pairedtag
A snakemake workflow for single-cell multiomic data analysis pipeline
Overview
Topics:
Latest release: None, Last update: 2023-07-19
Linting: linting: failed, Formatting:formatting: failed
Deployment
Step 1: Install Snakemake and Snakedeploy
Snakemake and Snakedeploy are best installed via the Mamba package manager (a drop-in replacement for conda). If you have neither Conda nor Mamba, it is recommended to install Miniforge. More details regarding Mamba can be found here.
When using Mamba, run
mamba create -c conda-forge -c bioconda --name snakemake snakemake snakedeploy
to install both Snakemake and Snakedeploy in an isolated environment. For all following commands ensure that this environment is activated via
conda activate snakemake
Step 2: Deploy workflow
With Snakemake and Snakedeploy installed, the workflow can be deployed as follows. First, create an appropriate project working directory on your system and enter it:
mkdir -p path/to/project-workdir
cd path/to/project-workdir
In all following steps, we will assume that you are inside of that directory. Then run
snakedeploy deploy-workflow https://github.com/jianhong/pairedtag . --tag None
Snakedeploy will create two folders, workflow
and config
. The former contains the deployment of the chosen workflow as a Snakemake module, the latter contains configuration files which will be modified in the next step in order to configure the workflow to your needs.
Step 3: Configure workflow
To configure the workflow, adapt config/config.yml
to your needs following the instructions below.
Step 4: Run workflow
The deployment method is controlled using the --software-deployment-method
(short --sdm
) argument.
To run the workflow with automatic deployment of all required software via conda
/mamba
, use
snakemake --cores all --sdm conda
Snakemake will automatically detect the main Snakefile
in the workflow
subfolder and execute the workflow module that has been defined by the deployment in step 2.
For further options such as cluster and cloud execution, see the docs.
Step 5: Generate report
After finalizing your data analysis, you can automatically generate an interactive visual HTML report for inspection of results together with parameters and code inside of the browser using
snakemake --report report.zip
Configuration
The following section is imported from the workflow’s config/README.md
.
General configuration
To configure this workflow, modify config/config.yaml
according to your needs, following the explanations provided in the file.
Sample and unit setup
The sample and unit setup is specified via tab-separated tabular files (.tsv
).
Missing values can be specified by empty columns or by writing NA
.
sample sheet
The default sample sheet is config/samples.tsv
(as configured in config/config.yaml
).
Each sample refers to an actual physical sample, and replicates (both biological and technical) may be specified as separate samples.
For each sample, you will always have to specify a sample_name
.
unit sheet
The default unit sheet is config/units.tsv
(as configured in config/config.yaml
).
For each sample, add one or more sequencing units (for example if you have several runs or lanes per sample).
.fastq
file source
For each unit, you will have to define a source for your .fastq
files.
This can be done via the columns fq1
, fq2
, with two .fastq
files for paired-end reads.
adapter trimming
TODO
Linting and formatting
Linting results
Workflow defines that rule get_genome is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule get_annotation is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule genome_faidx is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule bwa_index is eligible for caching between workflows (use the --cache argument to enable this).
Workflow defines that rule cellranger_index is eligible for caching between workflows (use the --cache argument to enable this).
Lints for snakefile /tmp/tmp8w84n72k/workflow/rules/common.smk:
* Path composition with '+' in line 9:
This becomes quickly unreadable. Usually, it is better to endure some
redundancy against having a more readable workflow. Hence, just repeat
common prefixes. If path composition is unavoidable, use pathlib or
(python >= 3.6) string formatting with f"...".
Also see:
Lints for snakefile /tmp/tmp8w84n72k/workflow/rules/ref.smk:
* Path composition with '+' in line 3:
This becomes quickly unreadable. Usually, it is better to endure some
redundancy against having a more readable workflow. Hence, just repeat
common prefixes. If path composition is unavoidable, use pathlib or
(python >= 3.6) string formatting with f"...".
... (truncated)
Formatting results
[DEBUG]
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/remap_barcode.smk": Formatted content is different from original
[DEBUG]
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/cellranger.smk": Formatted content is different from original
[DEBUG]
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/common.smk": Formatted content is different from original
[DEBUG]
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/qc.smk": Formatted content is different from original
[DEBUG]
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/rules/ref.smk": Formatted content is different from original
[DEBUG]
[DEBUG] In file "/tmp/tmp8w84n72k/workflow/Snakefile": Formatted content is different from original
[INFO] 6 file(s) would be changed 😬
snakefmt version: 0.8.4