Abstract
Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases. Existing deconvolution approaches can be divided into two major groups: supervised and unsupervised methods. Supervised deconvolution methods use cell type-specific prior information including cell proportions, reference cell type-specific gene signatures, or marker genes for each cell type, which may not be available in practice. Unsupervised methods, such as non-negative matrix factorization (NMF) and Convex Analysis of Mixtures (CAM), in contrast, completely disregard prior information and thus are not efficient for data with partial cell type-specific information. In this paper, we propose a semi-supervised deconvolution method, semi-CAM, that extends CAM by utilizing marker information from partial cell types. Analysis of simulation and two benchmark data have demonstrated that semi-CAM outperforms CAM by yielding more accurate cell proportion estimations when markers from partial/all cell types are available. In addition, when markers from all cell types are available, semi-CAM achieves better or similar accuracy compared to the supervised method using signature genes, CIBERSORT, and the marker-based supervised methods semi-NMF and DSA. Furthermore, analysis of human chlamydia-infection data with bulk expression profiles from six cell types and prior marker information of only three cell types suggests that semi-CAM achieves more accurate cell proportion estimations than CAM.
Highlights
Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases
To fully utilize partial cell-specific information, we propose a semi-supervised marker-guided deconvolution method, semi-Convex Analysis of Mixtures (CAM), to adopt partially known marker gene information in the deconvolution procedure
The results demonstrate that semi-CAM achieves more precise cell proportion estimations than the unsupervised deconvolution method CAM by incorporating marker genes for partial tissues/cells
Summary
Deconvolution of bulk transcriptomics data from mixed cell populations is vital to identify the cellular mechanism of complex diseases. Supervised deconvolution methods use cell typespecific prior information including cell proportions, reference cell type-specific gene signatures, or marker genes for each cell type, which may not be available in practice. Unsupervised methods, such as non-negative matrix factorization (NMF) and Convex Analysis of Mixtures (CAM), in contrast, completely disregard prior information and are not efficient for data with partial cell type-specific information. Analysis of human chlamydia-infection data with bulk expression profiles from six cell types and prior marker information of only three cell types suggests that semi-CAM achieves more accurate cell proportion estimations than CAM. CAM first identifies novel marker genes by geometrically locating the vertices of the simplex that most tightly encloses the bulk gene expression profiles, and estimates the cell proportions of the cell mixtures using the identified marker genes
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