bio-workflows-timecourse-pipeline

Name: bio-workflows-timecourse-pipeline
Rating: 5 (1 reviews)
Author: bg-szy

End-to-end time-course analysis from expression matrix to temporal patterns and enrichment. Covers temporal DE, Mfuzz soft clustering, optional rhythm detection, GAM trajectory fitting, and per-cluster pathway enrichment. Use when analyzing bulk time-series expression experiments from any omics platform.

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Updated 18 days ago

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Version Compatibility

Reference examples tested with: DESeq2 1.42+, clusterProfiler 4.10+, limma 3.58+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+, statsmodels 0.14+

Before using code patterns, verify installed versions match. If versions differ:

Python: pip show <package> then help(module.function) to check signatures
R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed p

[Description truncada. Veja o README completo no GitHub.]

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