Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors
Abstract
Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 20, 2023
- Source ID
- 10.1186/s13059-023-03077-7
Entities
People
- Ariel A. Hippen
- Casey S. Greene
- Dalia K. Omran
- Euihye Jung
- Jennifer A Doherty
- Lukas M. Weber
- Ronny Drapkin
- Stephanie C. Hicks
Organizations
- Adelson Foundation
- Division of Cancer Epidemiology and Genetics, National Cancer Institute
- Perelman School of Medicine at the University of Pennsylvania
- United States Department of Defense