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Characterizing Mutational Load and Clonal Composition of Human Blood
Published on: July 11, 2019
qcCHIP: an R package to identify clonal hematopoiesis variants using cohort-specific data characteristics
Xiang Liu1, Yi-Han Tang1,2, James Blachly3
1Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, United States.
Insights
Clonal hematopoiesis (CH) detection is improved with the new qcCHIP R package. This bioinformatics tool optimizes quality control for accurate CH identification across diverse datasets and cancer types.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Clonal hematopoiesis (CH) serves as a molecular biomarker linked to adverse health outcomes in both healthy individuals and those with existing conditions, including cancer.
- Current methods for CH detection rely on genomic sequencing and extensive bioinformatics data filtering.
Purpose of the Study:
- To introduce qcCHIP, an R package designed as a bioinformatics pipeline for enhanced CH detection.
- To implement permutation-based parameter optimization for quality control and cohort-specific CH identification.
Main Methods:
- The qcCHIP R package utilizes permutation-based parameter optimization.
- The pipeline is designed for quality control filtering and identifying clonal hematopoiesis within specific cohorts.
Main Results:
- qcCHIP was benchmarked across various data settings, including different sequencing depths and cohort sizes.
- Performance was evaluated with and without normal-tumor paired samples and across diverse cancer types.
Conclusions:
- qcCHIP enables customized analysis for CH detection based on specific cohort data characteristics.
- The R package facilitates robust and adaptable identification of clonal hematopoiesis.
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.
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