BioQC

BioQC detects tissue heterogeneity in high-throughput gene expression data to identify contamination from non-target tissues and improve data quality and reproducibility.

Key Features:

• Detection of Tissue Heterogeneity: Uses the Wilcoxon–Mann–Whitney test to identify discrepancies in gene expression profiles indicative of contamination by non-target tissues.
• Extensive Gene Signatures Database: Leverages over 150 tissue-enriched gene signatures derived from large-scale transcriptomics studies as reference signatures.
• Integration with Prior Knowledge: Incorporates a comprehensive database of tissue-specific signatures to enhance detection sensitivity for subtle heterogeneity.
• Scalability and Efficiency: Implements a scalable algorithm suitable for large-scale datasets, including applications to the Genotype-Tissue Expression (GTEx) project.
• Implementation: Provided as an R/Bioconductor software package for computational analysis of gene expression data.
• Validation Support: Predicts contamination events that have been confirmed by quantitative RT-PCR in case studies.

Scientific Applications:

• Quality control of transcriptomics data: Identifies tissue contamination to improve accuracy and reproducibility of gene expression analyses.
• Whole-organ profiling studies: Detects cross-tissue cellular admixture in whole-organ or complex tissue samples.
• Large-scale dataset assessment (e.g., GTEx): Reveals clustering patterns and potential tissue heterogeneity across extensive transcriptomic collections.

Methodology:

Implemented in R/Bioconductor; compares sample expression against a database of over 150 tissue-enriched gene signatures using the Wilcoxon–Mann–Whitney test and integrates prior knowledge with a scalable algorithm for large datasets.