2018
DOI: 10.1101/358366
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Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares

Abstract: Gene-expression deconvolution is used to quantify different types of cells in a mixed population. It provides a highly promising solution to rapidly characterize the tumor-infiltrating immune landscape and identify cold cancers. However, a major challenge is that gene-expression data are frequently contaminated by many outliers that decrease the estimation accuracy. Thus, it is imperative to develop a robust deconvolution method that automatically decontaminates data by reliably detecting and removing outliers… Show more

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Cited by 20 publications
(28 citation statements)
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References 33 publications
(38 reference statements)
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“…Fifteen bulk deconvolution methods a have been evaluated, including two traditional (ordinary least squares (OLS 21 ) and non-negative least squares (NNLS 22 )) and one weighted least squares method (EPIC 26 ); two robust regression (FARDEEP 46 , RLR 47 ), one support-vector regression (CIBERSORT 9 ) and four penalized regression (ridge, lasso, elastic net 48 and Digital Cell Quantifier (DCQ 29 )) approaches; one quadratic programming (DeconRNASeq 20 ), one method that models the problem in logarithmic scale (dtangle 39 ) and three methods included in the CellMix R package: 19 Digital Sorting Algorithm (DSA 17 ) and two semi-supervised non-negative matrix factorization methods (ssKL and ssFrobenius 18 ). Furthermore, five deconvolution methods that use scRNA-seq as reference have been evaluated: deconvSeq 49 , MuSiC 24 , DWLS 23 , Bisque 50 and SCDC 25 .…”
Section: Methodsmentioning
confidence: 99%
“…Fifteen bulk deconvolution methods a have been evaluated, including two traditional (ordinary least squares (OLS 21 ) and non-negative least squares (NNLS 22 )) and one weighted least squares method (EPIC 26 ); two robust regression (FARDEEP 46 , RLR 47 ), one support-vector regression (CIBERSORT 9 ) and four penalized regression (ridge, lasso, elastic net 48 and Digital Cell Quantifier (DCQ 29 )) approaches; one quadratic programming (DeconRNASeq 20 ), one method that models the problem in logarithmic scale (dtangle 39 ) and three methods included in the CellMix R package: 19 Digital Sorting Algorithm (DSA 17 ) and two semi-supervised non-negative matrix factorization methods (ssKL and ssFrobenius 18 ). Furthermore, five deconvolution methods that use scRNA-seq as reference have been evaluated: deconvSeq 49 , MuSiC 24 , DWLS 23 , Bisque 50 and SCDC 25 .…”
Section: Methodsmentioning
confidence: 99%
“…A comprehensive association analysis was performed between the survival and the tumour immune cell infiltrations in HGSOC by inferring the fractions of 22 immune cell types from tumour transcriptomes by using CIBERSORT [22]. The methods developed for genome data informed cell type quantification include CIBERSORT, xCell [33], TRUST (which was designed for RNA-seq data) [34] and a recently published algorithm, FARDEEP (Fast And Robust DEconvolution of Expression Profiles), that can provide the absolute cell abundance estimation [35]. Although the algorithms utilised in the abovementioned methods were different, they were developed for the same purpose.…”
Section: Discussionmentioning
confidence: 99%
“…CIBERSORT was a gene-based deconvolution algorithm developed by Newman et al and was applied to predict the abundance of immune cells using complex gene expression data in this investigation (18).…”
Section: Immune Landscape Of Tmementioning
confidence: 99%