bio-expression-matrix-metadata-joins

Version Compatibility

Reference examples tested with: pandas 2.2+

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.

Metadata Joins

Load Sample Metadata

Goal: Read sample metadata into a DataFrame aligned with the count matrix columns.

Approach: Load metadata CSV with sample IDs as the index, matching count matrix column names.

import pandas as pd

# Load metadata
metadata = pd.read_csv('sample_info.csv', index_col=0)

# Metadata should have samples as rows, attributes as columns
# Index should match count matrix column names

Basic Join

Goal: Align count matrix columns with metadata rows so samples are in matching order.

Approach: Find common samples between both data sources, subset and reorder to ensure alignment.

"Match my sample metadata to my count matrix" → Intersect sample identifiers between the count matrix and metadata, then reorder both to match.

import pandas as pd

# Count matrix: genes x samples
counts = pd.read_csv('counts.tsv', sep='\t', index_col=0)

# Metadata: samples x attributes
metadata = pd.read_csv('metadata.csv', index_col=0)

# Ensure sample order matches
common_samples = counts.columns.intersection(metadata.index)
counts = counts[common_samples]
metadata = metadata.loc[common_samples]

# Verify alignment
assert all(counts.columns == metadata.index)

Handle Sample Name Mismatches

Goal: Identify and resolve discrepancies between count matrix column names and metadata row names.

Approach: Report samples present in only one data source and subset to the intersection.

def harmonize_sample_names(counts, metadata):
    '''Match sample names between counts and metadata.'''
    count_samples = set(counts.columns)
    meta_samples = set(metadata.index)

    common = count_samples & meta_samples
    only_counts = count_samples - meta_samples
    only_meta = meta_samples - count_samples

    if only_counts:
        print(f'Samples in counts but not metadata: {only_counts}')
    if only_meta:
        print(f'Samples in metadata but not counts: {only_meta}')

    counts = counts[sorted(common)]
    metadata = metadata.loc[sorted(common)]
    return counts, metadata

counts, metadata = harmonize_sample_names(counts, metadata)