bio-workflows-timecourse-pipeline

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 package and adapt the example to match the actual API rather than retrying.

Time-Course Analysis Pipeline

"Analyze my time-course expression data end-to-end" → Orchestrate temporal differential expression, Mfuzz soft clustering, optional circadian rhythm detection, GAM trajectory fitting, changepoint detection, and per-cluster pathway enrichment.

Complete workflow from expression matrix through temporal differential expression, soft clustering, optional rhythm detection, trajectory fitting, and per-cluster pathway enrichment.

Pipeline Overview

Expression matrix + time metadata
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    v
[1. Temporal DE] ---------> limma splines / DESeq2 LRT
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[2. Filter] --------------> Significant temporal genes (FDR <0.05)
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[3. Mfuzz Clustering] ----> Soft clustering of expression profiles
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    |                            +---> QC: membership >0.5, no empty clusters
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    +--- Circadian design? ---> [4a. Rhythm Detection] (MetaCycle / CosinorPy)
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    |                               v
    |                           Rhythmic genes + period/phase estimates
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[4b. GAM Trajectory] -----> mgcv GAM fitting for top clusters
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[5. Pathway Enrichment] --> clusterProfiler per-cluster GO/KEGG
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    v
Temporal gene modules + enriched pathways + trajectory plots

Step 1: Temporal Differential Expression

R (limma splines)

library(limma)
library(splines)

expr <- as.matrix(read.csv('counts_normalized.csv', row.names = 1))
meta <- read.csv('metadata.csv')

time_points <- meta$time
design <- model.matrix(~ ns(time_points, df = 3))

fit <- lmFit(expr, design)
fit <- eBayes(fit)

# Test all spline coefficients jointly for temporal significance
temporal_results <- topTable(fit, coef = 2:ncol(design), number = Inf, sort.by = 'F')
# topTable already returns adj.P.Val (BH-corrected); use it directly