SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data

被引：1

作者：

Liu, Yunqing ^{[1
]}

Li, Ningshan ^{[1
,2
,3
]}

Qi, Ji ^{[1
]}

Xu, Gang ^{[1
,4
]}

Zhao, Jiayi ^{[1
]}

Wang, Nating ^{[1
]}

Huang, Xiayuan ^{[1
]}

Jiang, Wenhao ^{[1
]}

Wei, Huanhuan ^{[1
,5
]}

Justet, Aurelien ^{[5
,6
]}

Adams, Taylor S. ^{[5
]}

Homer, Robert ^{[7
]}

Amei, Amei ^{[4
]}

Rosas, Ivan O. ^{[8
]}

Kaminski, Naftali ^{[5
]}

Wang, Zuoheng ^{[1
,9
]}

Yan, Xiting ^{[1
,5
]}

机构：

[1] Yale Sch Publ Hlth, Dept Biostat, New Haven, CT 06510 USA

[2] Shanghai Jiao Tong Univ, SJTU Yale Join Ctr Biostat & Data Sci, Sch Life Sci & Biotechnol, Dept Bioinformat & Biostat, Shanghai, Peoples R China

[3] Chinese Univ Hong Kong, Affiliated Hosp 2, Shenzhen, Guangdong, Peoples R China

[4] Univ Nevada, Dept Math Sci, Las Vegas, NV USA

[5] Yale Sch Med, Sect Pulm Crit Care & Sleep Med, New Haven, CT 06510 USA

[6] Normandie Univ, CHU Caen UNICAEN, Ctr Competences Malad Plum Rares, Serv Pneumol,CEA,CNRS,ISTCT,CERVOxy Grp,GIP CYCERO, Caen, France

[7] Yale Sch Med, Dept Pathol, New Haven, CT USA

[8] Baylor Coll Med, Dept Med, Houston, TX USA

[9] Yale Sch Med, Dept Biomed Informat & Data Sci, New Haven, CT 06510 USA

来源：

GENOME BIOLOGY | 2024年 / 25卷 / 01期

基金：

美国国家卫生研究院;

关键词：

SINGLE-CELL; GENE-EXPRESSION; ATLAS; SEQ;

D O I：

10.1186/s13059-024-03416-2

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Spatial barcoding-based transcriptomic (ST) data require deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER tackles platform effects between ST and scRNA-seq data, ensuring a linear relationship between them while addressing sparsity and spatial correlations in cell types across capture spots. SDePER estimates cell-type proportions, enabling enhanced resolution tissue mapping by imputing cell-type compositions and gene expressions at unmeasured locations. Applications to simulated data and four real datasets showed SDePER's superior accuracy and robustness over existing methods.

引用

页数：28

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