mushu-bytes/scelect
Snakemake Pipeline for S(c)electing Normalization and Integration Methods for Single Cell Analysis
Overview
Latest release: None, Last update: 2024-06-14
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/mushu-bytes/scelect . --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.
Describe how to configure the workflow (using config.yaml and maybe additional files). All of them need to be present with example entries inside of the config folder.
Linting and formatting
Linting results
1Lints for snakefile /tmp/tmpfcl2sgqg/workflow/Snakefile:
2 * Absolute path "/mnt/shared/nationwide/cell_type_datasets" in line 8:
3 Do not define absolute paths inside of the workflow, since this renders
4 your workflow irreproducible on other machines. Use path relative to the
5 working directory instead, or make the path configurable via a config
6 file.
7 Also see:
8 https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
9 * Absolute path "/data/{SCANPY_DATA} " in line 33:
10 Do not define absolute paths inside of the workflow, since this renders
11 your workflow irreproducible on other machines. Use path relative to the
12 working directory instead, or make the path configurable via a config
13 file.
14 Also see:
15 https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
16 * Absolute path "/results " in line 34:
17 Do not define absolute paths inside of the workflow, since this renders
18 your workflow irreproducible on other machines. Use path relative to the
19 working directory instead, or make the path configurable via a config
20 file.
21 Also see:
22 https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
23 * Absolute path "/scripts/r_scripts/scelect_seurat.R " in line 53:
24 Do not define absolute paths inside of the workflow, since this renders
25 your workflow irreproducible on other machines. Use path relative to the
26 working directory instead, or make the path configurable via a config
27 file.
28 Also see:
29 https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
30 * Absolute path "/data/{SEURAT_DATA} " in line 54:
31 Do not define absolute paths inside of the workflow, since this renders
32 your workflow irreproducible on other machines. Use path relative to the
33 working directory instead, or make the path configurable via a config
34 file.
35 Also see:
36 https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
37 * Absolute path "/results " in line 55:
38 Do not define absolute paths inside of the workflow, since this renders
39 your workflow irreproducible on other machines. Use path relative to the
40 working directory instead, or make the path configurable via a config
41 file.
42 Also see:
43 https://snakemake.readthedocs.io/en/latest/snakefiles/configuration.html#configuration
44
45Lints for rule scanpy (line 24, /tmp/tmpfcl2sgqg/workflow/Snakefile):
46 * No log directive defined:
47 Without a log directive, all output will be printed to the terminal. In
48 distributed environments, this means that errors are harder to discover.
49 In local environments, output of concurrent jobs will be mixed and become
50 unreadable.
51 Also see:
52 https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
53
54Lints for rule seurat (line 60, /tmp/tmpfcl2sgqg/workflow/Snakefile):
55 * No log directive defined:
56 Without a log directive, all output will be printed to the terminal. In
57 distributed environments, this means that errors are harder to discover.
58 In local environments, output of concurrent jobs will be mixed and become
59 unreadable.
60 Also see:
61 https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#log-files
Formatting results
1[DEBUG]
2[DEBUG] In file "/tmp/tmpfcl2sgqg/workflow/Snakefile": Formatted content is different from original
3[INFO] 1 file(s) would be changed 😬
4
5snakefmt version: 0.10.2