2013/10/25 by Niya Wang, Eric P. Hoffman, Wang, Niya +17
Biochemistry, Genetics and Molecular Biology · #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning (stat.ML) #Molecular Biology Techniques and Applications #Quantitative Methods (q-bio.QM) #Single-cell and spatial transcriptomics
paper · pdf · doi:10.48550/arxiv.1310.7033
openalex publication_date 2013/10/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we ask whether it is possible to deconvolute two-source mixed expressions (estimating both proportions and cell-specific profiles) from two or more heterogeneous samples without requiring any prior knowledge. Supported by a well-grounded mathematical framework, we argue that both constituent proportions and cell-specific expressions can be estimated in a completely unsupervised mode when cell-specific marker genes exist, which do not have to be known a priori, for each of constituent cell types. We demonstrate the performance of unsupervised deconvolution on both simulation and real gene expression data, together with perspective discussions.