numeric-range-validation-bioinformatics

numeric-range-validation-bioinformatics

Summary

Validate that computed quantitative genomic features (e.g., insulation scores, contact frequencies) fall within mathematically and biologically expected numeric ranges and that derived boolean annotations (e.g., TAD boundary flags) are correctly typed. This skill ensures data integrity and identifies computational or thresholding errors before downstream analysis.

When to use

After applying a quantitative analysis function (e.g., cooltools.insulation, contact frequency calculations) to Hi-C cooler files or other genomic datasets, validate that the output numeric columns contain values within plausible ranges (e.g., insulation scores as floats, not NaN unless expected; boundary calls as strict boolean 0/1 or True/False) and that no unexpected outliers or type mismatches have been introduced.

When NOT to use

• Input data are already a quality-controlled, published feature table from a trusted source (e.g., GEO deposit with peer-reviewed validation).
• Analysis goal is exploratory or hypothesis-generating and does not require strict quality gates.
• Numeric values are intentionally sparse or contain expected NaN by design (e.g., regions filtered for low coverage); in such cases, validate sparsity pattern separately rather than enforcing full range.

Inputs

• pandas DataFrame with computed Hi-C features (insulation scores, contact frequencies, or similar)
• cooler file (HDF5 contact matrix) with bin metadata
• window size parameter (integer, in base pairs) used in feature computation

Outputs

• validated pandas DataFrame with confirmed dtype and value range integrity
• BED format file with validated boundary annotations (optional)
• validation report or assertion log confirming range and type checks passed

How to apply

Load the output table (typically as a pandas DataFrame) and inspect the dtype and value ranges of numeric columns. For insulation scores, verify that values are floating-point numbers without unexpected NaN clusters unrelated to low-coverage regions, and that the range is bounded by the definition of the metric (e.g., normalized ratios should not exceed physically plausible limits). For boundary annotations, confirm that is_boundary_* columns are strictly boolean (dtype: bool or int 0/1). Check that row counts match expected bin coordinates and that required columns (e.g., region1, region2, insulation_score, is_boundary_{window}) are all present. Use pandas describe(), dtypes inspection, and assertions on min/max values to programmatically enforce these invariants before exporting to BED or other downstream formats.