bio-expression-matrix-sparse-handling

bio-expression-matrix-sparse-handling is a Bioinformatics & life sciences skill for AI agents, published by GPTomics in the bioSkills repository on GitHub. Skillspace rates bio-expression-matrix-sparse-handling C (79/100) and it passed the safety scan; the rating is based on static analysis only.

Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

Grade

C

Quality

79 / 100

Safety

Passed

GitHub stars

1,025

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