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bio-expression-matrix-sparse-handling

Maintainer FreedomIntelligence · Last updated April 1, 2026

Handle sparse count matrices efficiently with scipy sparse formats.

OpenClawNanoClawAnalysisReproductionbio-expression-matrix-sparse-handling🧠 bioos extended suitebioos extended bioinformatics suitehandle

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-expression-matrix-sparse-handling

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

def check_sparsity(counts): zeros = (counts == 0).sum().sum() total = counts.size sparsity = zeros / total print(f'Sparsity: {sparsity:.1%} ({zeros:,} / {total:,} zeros)') return sparsity.
dense_df = pd.read_csv('counts.csv', index_col=0) sparse_matrix = sp.csr_matrix(dense_df.values).

Source Doc

Excerpt From SKILL.md

Convert Dense to Sparse

import scipy.sparse as sp
import pandas as pd

## Keep row/column names

gene_names = dense_df.index.tolist()
sample_names = dense_df.columns.tolist()

## CSC (Compressed Sparse Column): efficient column slicing

sparse_csr = sp.csr_matrix(dense_df.values)  # Row-oriented
sparse_csc = sp.csc_matrix(dense_df.values)  # Column-oriented

Use cases

Use bio-expression-matrix-sparse-handling for genomics and bioinformatics workflows.
Apply bio-expression-matrix-sparse-handling to sequencing, variant, or omics analysis tasks.