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Semi-CAM: A semi-supervised deconvolution method for bulk transcriptomic data with partial marker gene information.
- Source :
-
Scientific Reports . 3/25/2020, Vol. 10 Issue 1, p1-12. 12p. - Publication Year :
- 2020
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 10
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 142408888
- Full Text :
- https://doi.org/10.1038/s41598-020-62330-2