Open AccessswCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution
Author(s) -
Lulu Chen,
Wu Chao,
Chia-Hsiang Lin,
Rujia Dai,
Chunyu Liu,
Robert Clarke,
Guoqiang Yu,
Jennifer E. Van Eyk,
David M. Herrington,
Yue Wang
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab839
Subject(s) - deconvolution , hyperparameter , computer science , expression (computer science) , non negative matrix factorization , population , sample (material) , blind deconvolution , pattern recognition (psychology) , underdetermined system , algorithm , matrix decomposition , mathematics , biology , artificial intelligence , eigenvalues and eigenvectors , physics , quantum mechanics , thermodynamics , programming language , demography , sociology
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.