Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors.

ABSTRACT:

Background

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.

Results

We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-c

SUBMITTER: Hippen AA 

PROVIDER: S-EPMC10588129 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature