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bioRxiv · 10.1101/2021.06.30.450493

Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion

Wang, W.·Yao, J.·Wang, Y.·Zhang, C.·Tao, W.·Zou, J.·Ni, T.
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Abstract

Single cell RNA-seq (scRNA-seq) has been widely used to uncover cellular heterogeneity, however, the constraints of cost make it impractical as a routine on large patient cohorts. Here we present ENIGMA, a method that accurately deconvolute bulk tissue RNA-seq into single cell-type resolution given the knowledge gained from scRNA-seq. ENIGMA applies a matrix completion strategy to minimize the distance between mixture transcriptome and weighted combination of cell type-specific expression, allowing quantification of cell type proportions and reconstruction of cell type-specific transcriptome. The superior performance of ENIGMA was validated in simulated and realistic datasets, including disease-related tissues, demonstrating its ability in novel biological findings.

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Wang, W., Yao, J., Wang, Y., Zhang, C., Tao, W., Zou, J., Ni, T.. 2021-07-01. Improved estimation of cell type-specific gene expression through deconvolution of bulk tissues with matrix completion. https://doi.org/10.1101/2021.06.30.450493

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