rsomics-sc-normalize

Library-size normalization plus log1p of a single-cell count matrix, numerically matching scanpy's sc.pp.normalize_total followed by sc.pp.log1p.

Each cell is scaled so its total count equals a target. By default the target is the median of every cell's total count (computed over all cells, including cells with zero counts), reproducing scanpy's target_sum=None. Then ln(1 + x) is applied to the scaled values. The sparsity pattern is preserved exactly because log1p(0) = 0.

Usage

# scanpy default: median target, log1p
rsomics-sc-normalize filtered_feature_bc_matrix/ -o norm.mtx

# CPM (target 1e6), no log
rsomics-sc-normalize mtx_dir/ --target-sum 1e6 --no-log -o cpm.mtx

Input is a 10x MTX directory (matrix.mtx or matrix.mtx.gz, genes × cells). Output is a MatrixMarket coordinate matrix in the same genes × cells layout with real values.

--exclude-highly-expressed (scanpy's optional flag, which drops genes exceeding max_fraction of any cell's counts before computing the size factor) is not implemented: it is rarely used in routine pipelines and adds a two-pass gene-masking step. The size factor here is always the cell's full total count.

Origin

This crate is an independent Rust reimplementation of scanpy's normalize_total + log1p based on:

• The published method (Wolf, Angerer & Theis, "SCANPY: large-scale single-cell gene expression data analysis", Genome Biology 2018, doi:10.1186/s13059-017-1382-0).
• The public MatrixMarket and 10x Genomics matrix file-format specs.
• Black-box behavior testing against the scanpy Python package.

License: MIT OR Apache-2.0. Upstream credit: scanpy https://github.com/scverse/scanpy (BSD-3-Clause).