bio-temporal-genomics-circadian-rhythms

Version Compatibility

Reference examples tested with: R stats (base), pandas 2.2+, 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.

Circadian Rhythm Detection

"Test which genes in my time-course data have circadian rhythms" → Fit cosinor models at a specified period (typically 24h) to expression time series, estimating amplitude, phase (acrophase), and rhythmicity significance for each gene.

• Python: CosinorPy.cosinor.fit_group() for cosinor regression
• R: MetaCycle::meta2d() for multi-method rhythmicity testing (JTK_CYCLE + ARSER)

Identifies periodic gene expression patterns at known periods (typically 24h) using cosinor regression, non-parametric rhythmicity tests, and meta-analysis approaches combining multiple methods.

Core Workflow

1. Prepare time-series expression matrix (genes x timepoints)
2. Fit cosinor models or apply rhythmicity tests at specified period
3. Extract rhythm parameters: amplitude, phase (acrophase), p-value
4. Correct for multiple testing (BH FDR)
5. Filter significant rhythmic genes and characterize phase distribution

CosinorPy (Python)

Goal: Test for circadian rhythmicity in time-series expression data by fitting cosinor models at a known period (typically 24h) and estimating amplitude, phase, and significance.

Approach: Fit single- or multi-component cosine curves to each gene's expression profile using CosinorPy, apply batch processing across all genes, and correct p-values with BH FDR to identify significant oscillators.

Single-Component Cosinor

Fits y = M + A*cos(2*pi*t/T + phi) where M = MESOR, A = amplitude, phi = acrophase.

import pandas as pd
from cosinorpy import file_parser, cosinor, cosinor1

df = file_parser.read_csv('expression_timecourse.csv')

# Single-component cosinor fit for one gene
# period=24: standard circadian period in hours
# fit_me takes X (time) and Y (expression) arrays, returns a tuple
gene_data = df[df['test'] == 'test_gene']
res = cosinor.fit_me(gene_data['x'].values, gene_data['y'].values, period=24, n_components=1)