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Internal guide for the microbiome causal inference pipeline.

mbcausal profiles shotgun metagenomes, turns them into a CLR feature matrix, factorizes it into named groups of taxa, and runs lagged causal discovery over clinical outcomes on IBD cohorts (iHMP and others).

Warning

Not all stages are built. Profiling, the data layer, the preprocessor, the NMF representation and the lagged causal discovery suite are implemented and tested. The predictor and evaluator stages are still stubs, as is the autoencoder representation.

Pages

• Installation, create the env and install the package
• Configuration, paths and run settings (mbcausal.yaml)
• Profiling, reads to abundance tables
• Preprocessing, cohort tables, QC and the CLR feature matrix
• Discovery, NMF factors, the eligibility screen and lagged effects
• Simulation, cohorts where the answer is known
• Reproduction, the exact commands behind the paper's numbers

Subcommand

profile          fastq            -> per sample profiler reports
build-cohort     reports          -> abundance.csv + sample_table.csv with qc_pass
preprocess       cohort tables    -> CLR feature matrix
discover         feature matrix   -> NMF factors, eligibility screen, lagged estimates
sim              nothing          -> cohorts whose answer we chose in advance

Repo

• package: src/mbcausal/
• simulation study: src/mbcausal/sim/
• checks: make check (pytest, ruff, mypy)