Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 6, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab337
Keywords
linear mixed model; prostate cancer; transcriptomics; RNA-seq; multiple sampling; variance
Funding
- Prostate Cancer Foundation (Young Investigator Awards)
- Department of Defense Prostate Cancer Research Program [W81XWH-19-1-0712, W81XWH-16-1-0433]
- Intramural Research Program of the NIH, National Cancer Institute
Ask authors/readers for more resources
Intratumoral heterogeneity is a known feature of human cancers that affects the analysis of gene expression differences. Repeated sampling of the same tumor can increase the statistical power of gene expression analysis and improve understanding of intratumoral variance.
Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one's understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available