ArticleBioinformatics (Oxford, England)2018
Computation and application of tissue-specific gene set weights.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Gene set optimization for cancer transcriptomics using sparse principal component analysis.bioRxiv : the preprint server for biology · 2025Article
- Gene set optimization for single cell transcriptomics.Computational intelligence methods for bioinformatics and biostatistics : ... international meeting, CIBB ... : revised selected papers. CIBB (Meeting) · 2025Article
- Leveraging cell type-specificity for gene set analysis of single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.BMC pharmacology & toxicology · 2024Article
- Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.PLoS computational biology · 2024Article
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.bioRxiv : the preprint server for biology · 2023Article
- Optimized Transcriptional Signature for Evaluation of MEK/ERK Pathway Baseline Activity and Long-Term Modulations in Ovarian Cancer.International journal of molecular sciences · 2022Article
- Prognostic value of Glypican family genes in early-stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy and possible mechanisms.BMC gastroenterology · 2020Article
- Transcriptomic Differences between Primary Colorectal Adenocarcinomas and Distant Metastases Reveal Metastatic Colorectal Cancer Subtypes.Cancer research · 2019Article
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1 author.
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Abstract
Motivation: Gene set testing, or pathway analysis, has become a critical tool for the analysis of high-dimensional genomic data. Although the function and activity of many genes and higher-level processes is tissue-specific, gene set testing is typically performed in a tissue agnostic fashion, which impacts statistical power and the interpretation and replication of results. Results: To address this challenge, we have developed a bioinformatics approach to compute tissue-specific weights for individual gene sets using information on tissue-specific gene activity from the Human Protein Atlas (HPA). We used this approach to create a public repository of tissue-specific gene set weights for 37 different human tissue types from the HPA and all collections in the Molecular Signatures Database. To demonstrate the validity and utility of these weights, we explored three different applications: the functional characterization of human tissues, multi-tissue analysis for systemic diseases and tissue-specific gene set testing. Availability and implementation: All data used in the reported analyses is publicly available. An R implementation of the method and tissue-specific weights for MSigDB gene set collections can be downloaded at http://www.dartmouth.edu/∼hrfrost/TissueSpecificGeneSets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.