sparse-matrix-validation

sparse-matrix-validation

Summary

Validation of sparse count matrices generated from single-cell ATAC-seq fragment data to ensure matrix shape, sparsity structure, and quantitative correctness before downstream analysis. This skill verifies that tile-based or peak-based count matrices possess expected dimensions, contain appropriately distributed non-zero entries across cells and genomic features, and reflect genuine chromatin accessibility signal.

When to use

After generating a count matrix from fragment data using pp.add_tile_matrix, pp.make_peak_matrix, or pp.make_gene_matrix in SnapATAC2. Validation is essential whenever you transition from fragment-level preprocessing to matrix-based analysis (dimension reduction, clustering, peak calling) to detect generation errors, parameter misconfigurations, or data corruption that would propagate through the pipeline.

When NOT to use

• Input is already a validated feature table (e.g., pre-computed matrix from a completed SnapATAC2 workflow); re-validation is redundant.
• Fragment file has not yet been imported into AnnData; validate fragment import separately before matrix generation.
• You are performing quality control on raw sequencing reads prior to alignment; use fragment-level QC (e.g., mapping rate, duplicate removal) instead.

Inputs

• AnnData object with .obsm['fragment_paired'] or .obsm['fragment_single'] containing aligned fragment data
• Tile coordinate definitions or peak BED file used for matrix generation
• Matrix generation parameters (counting strategy, bin size or peak coordinates)

Outputs

• Validated sparse count matrix (n_obs × n_vars) stored in .X or .obsm
• Summary statistics: matrix dimensions, sparsity fraction, entry value range, non-zero count distribution across cells and features

How to apply

Inspect the resulting matrix object for three key invariants: (1) verify matrix shape (n_obs × n_vars) matches the number of cells (observations) and genomic tiles or peaks (variables) as defined by your binning or peak-calling parameters; (2) confirm the matrix contains non-zero entries distributed across both cells and genomic features, indicating signal is not concentrated in a pathological subset; (3) validate sparsity is appropriate for ATAC-seq (typically 85–99% sparse depending on sequencing depth and genome resolution), and that entry values are counts (non-negative integers). These checks catch mismatches between fragment input and matrix construction parameters (e.g., wrong genome build, misaligned tile coordinates, or empty fragment sets).