bio-temporal-genomics-temporal-clustering

Version Compatibility

Reference examples tested with: numpy 1.26+, scanpy 1.10+, scikit-learn 1.4+

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.

Temporal Gene Clustering

"Group my time-course genes by expression pattern shape" → Cluster temporally variable genes into co-expression modules by trajectory shape using fuzzy c-means (Mfuzz), hierarchical methods, or DTW-based approaches, revealing coordinated response patterns.

• R: Mfuzz::mfuzz() for soft (fuzzy) temporal clustering
• Python: sklearn.cluster.KMeans on z-scored time profiles for hard clustering

Groups genes with similar temporal expression dynamics into clusters, revealing shared regulatory programs and coordinated response patterns across time-course experiments.

Core Workflow

1. Select temporally variable genes (pre-filtered by DE or variance)
2. Standardize expression profiles (z-score across timepoints)
3. Choose clustering method and number of clusters
4. Assign genes to clusters (hard or soft membership)
5. Validate clusters and run functional enrichment per cluster

Mfuzz (R/Bioconductor)

Goal: Group temporally variable genes into co-expression clusters by trajectory shape using fuzzy c-means, revealing shared regulatory programs.

Approach: Create an ExpressionSet from the time-series matrix, filter low-variance genes, standardize profiles, estimate the fuzzifier parameter, then run fuzzy c-means to assign soft cluster memberships.

Soft (fuzzy) c-means clustering assigns genes membership scores across all clusters, capturing genes with ambiguous temporal behavior.

Setup and Preprocessing

library(Mfuzz)
library(Biobase)

# Rows = genes, columns = timepoints (mean across replicates)
expr_mat <- as.matrix(read.csv('temporal_expression.csv', row.names = 1))

# Create ExpressionSet
eset <- ExpressionSet(assayData = expr_mat)

# filter.std removes genes with near-zero variance across timepoints
# min.std=0.5: removes flat genes; adjust based on data spread
eset <- filter.std(eset, min.std = 0.5)

# Standardize each gene to mean=0, sd=1 across timepoints
eset <- standardise(eset)