ArticlePLoS computational biology2024
Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 2 citations in OpenAlex.
- Functional Depth Biomarkers Distinguish Lung Squamous Cell Carcinoma from Lung Adenocarcinoma.Research square · 2026Article
- A kernel density estimation-based approach for quantifying O-GlcNAcylation dysregulation in cancer from gene expression data.Bioinformatics advances · 2026Article
- Gene set optimization for cancer transcriptomics using sparse principal component analysis.bioRxiv : the preprint server for biology · 2025Article
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Abstract
We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.
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