swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution
Abstract
Complex biological tissues are often a heterogeneous mixture of several molecularly distinct cell subtypes. Both subtype compositions and subtype-specific (STS) expressions can vary across biological conditions. Computational deconvolution aims to dissect patterns of bulk tissue data into subtype compositions and STS expressions. Existing deconvolution methods can only estimate averaged STS expressions in a population, while many downstream analyses such as inferring co-expression networks in particular subtypes require subtype expression estimates in individual samples. However, individual-level deconvolution is a mathematically underdetermined problem because there are more variables than observations.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 14, 2021
- Source ID
- 10.1093/bioinformatics/btab839
Entities
People
- Chia-hsiang Lin
- Chiung-ting Wu
- Chunyu Liu
- David M. Herrington
- Guoqiang Yu
- Jennifer E Van Eyk
- Lulu Chen
- Robert Clarke
- Rujia Dai
- Yue Wang
Organizations
- Cedars-Sinai Medical Center
- National Cheng Kung University
- National Institutes of Health
- State University of New York Upstate Medical University
- United States Department of Defense
- University of Minnesota
- Virginia Tech
- Wake Forest University