Snakemake中使用input函数编写rule all的方法咨询

Got it, let's break this down clearly. Whether you're working with a classic split of case/control groups or want every sample paired with every other sample, Snakemake's expand() function and list comprehensions are your best tools here. Here's how to set this up:

1. First, Define Your Sample Sets #

Start by listing your samples—you can do this directly in your Snakefile or pull from a config file for cleaner organization.

Option A: Split Case/Control Groups (Classic Setup) #

If you have distinct case and control cohorts (e.g., 5 cases, 3 controls), define them separately:

# In your Snakefile
CASES = ["case_01", "case_02", "case_03", "case_04", "case_05"]
CONTROLS = ["ctrl_01", "ctrl_02", "ctrl_03"]

Option B: All Samples Paired with All Samples #

If you want every sample to act as a "case" against every other sample (including itself, if needed), define a single sample list:

# In your Snakefile
ALL_SAMPLES = ["sample_A", "sample_B", "sample_C", "sample_D"]

2. Create Your Case-Control Comparison Rule #

First, write the rule that handles the actual comparison between a single case and single control. This rule will use wildcards for case and control to stay flexible:

rule case_control_compare:
    input:
        case="raw_data/{case}.fastq",  # Adjust path/extension to match your input files
        control="raw_data/{control}.fastq"
    output:
        "results/{case}_vs_{control}.txt"  # Adjust output path/format as needed
    shell:
        """
        # Replace this with your actual comparison command
        # Example: a hypothetical tool that compares two samples
        run_comparison_tool --case {input.case} --control {input.control} --output {output}
        """

3. Write rule all to Trigger All Comparisons #

Now use expand() (or list comprehensions for more control) to generate every required output file, which will tell Snakemake to run all necessary case_control_compare jobs.

For Split Case/Control Groups #

This will generate every combination of a case sample paired with a control sample:

rule all:
    input:
        expand("results/{case}_vs_{control}.txt", case=CASES, control=CONTROLS)

For All Samples Paired with All Samples #

Include self-comparisons (e.g., sample_A vs sample_A)

rule all:
    input:
        expand("results/{case}_vs_{control}.txt", case=ALL_SAMPLES, control=ALL_SAMPLES)

Exclude self-comparisons (only sample_A vs sample_B, sample_A vs sample_C, etc.)

Use a list comprehension to filter out pairs where case == control:

rule all:
    input:
        [
            f"results/{case}_vs_{control}.txt" 
            for case in ALL_SAMPLES 
            for control in ALL_SAMPLES 
            if case != control
        ]

Quick Notes to Avoid Headaches #

• Make sure your wildcard names (case/control) match exactly across rule all, your comparison rule, and file paths.
• If your output files use a different naming pattern (e.g., results/comparisons/{case}-{control}.vcf), just update the string in expand() or the list comprehension to match.
• For large sample sets, full pairwise comparisons will create N^2 jobs—double-check that this is actually what you need before running, as it can get computationally heavy.

内容的提问来源于stack exchange，提问作者mau_who