qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics

Abstract

Summary: Clonal hematopoiesis (CH) is a molecular biomarker associated with various adverse outcomes in both healthy individuals and those with underlying conditions, including cancer. Detecting CH usually involves genomic sequencing of individual blood samples followed by robust bioinformatics data filtering. We report an R package, qcCHIP, a bioinformatics pipeline that implements permutation-based parameter optimization to guide quality control filtering and cohort-specific CH identification. We benchmark qcCHIP under various data settings, including different sequencing depths, ranges of cohort sizes, with and without normal-tumor paired samples, and across different cancer types. We show that qcCHIP allows users to customize analysis needs to generate CH calls based on cohort-specific data characteristics.

Availability and implementation: qcCHIP R package is freely accessible at GitHub https://github.com/tenglab/qcCHIP and DOI: 10.5281/zenodo.16421861.

Figure 1.

Permutation-based approach to optimize clonal hematopoiesis (CH) quality control metric cutoffs. (a) Quality metrics to filter CH mutations by qcCHIP. (b) Permutation analysis and (c) precision and recall at different variant allele frequency (VAF) cutoffs using blood-only whole exome sequencing data from the ORIEN breast cancer cohort. Permutation consistency is the proportion of CH calls from the full cohort that are also called in the permuted subsets. Each point represents average consistency over 100 permutations. Colors indicate different permuted sample sizes. (d) Permutation analysis and (e) precision and recall at different VAF cutoffs using blood-tumor paired samples for the same breast cancer cohort. (f) Permutation analysis and (g) precision-VAF curves using blood-only ultra-high-depth targeted exon sequencing from an independent cohort of patients with breast cancer. Minor effects on recall are showed in Fig. 2, available as supplementary data at Bioinformatics online. The point sizes represent the number of common CH mutations between qcCHIP identification and manually curated CH mutations.