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Post‐modified non‐negative matrix factorization for deconvoluting the gene expression profiles of specific cell types from heterogeneous clinical samples based on RNA‐sequencing data
Author(s) -
Liu Yuan,
Liang Yu,
Kuang Qifan,
Xie Fanfan,
Hao Yingyi,
Wen Zhining,
Li Menglong
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2929
Subject(s) - rna , gene expression , gene , matrix (chemical analysis) , biology , microbiology and biotechnology , genetics , computational biology , chemistry , chromatography
The application of supervised algorithms in clinical practice has been limited by the lack of information on pure cell types. Several supervised algorithms have been proposed to estimate the gene expression patterns of specific cell types from heterogeneous samples. Post‐modified non‐negative matrix factorization (NMF), the unsupervised algorithm we proposed here, is capable of estimating the gene expression profiles and contents of the major cell types in cancer samples without any prior reference knowledge. Post‐modified NMF was first evaluated using simulation data sets and then applied to deconvolution of the gene expression profiles of cancer samples. It exhibited satisfactory performance with both the validation and application data. For application in 3 types of cancer, the differentially expressed genes (DEGs) identified from the deconvoluted gene expression profiles of tumor cells were highly associated with the cancer‐related gene sets. Moreover, the estimated proportions of tumor cells showed significant difference between the 2 compared patient groups in clinical endpoints. Our results indicated that the post‐modified NMF can efficiently extract the gene expression patterns of specific cell types from heterogeneous samples for subsequent analysis and prediction, which will greatly benefit clinical prognosis.