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. We developed a new machine learning tool, Fast And Robust DEconvolution of Expression Profiles (FARDEEP), to enumerate immune cell subsets from whole tumor tissue samples. To reduce noise in the tumor gene expression datasets, FARDEEP utilizes an adaptive least trimmed square to automatically detect and remove outliers before estimating the cell compositions. We show that FARDEEP is less susceptible to outliers and returns a better estimation of coefficients than the existing methods with both numerical simulations and real datasets. FARDEEP provides the absolute quantitation of each immune cell subset in addition to relative percentages. Hence, FARDEEP represents a novel robust algorithm to complement the existing toolkit for the characterization of tissueinfiltrating immune cell landscape. The source code for FARDEEP as implemented in R is available for download at https://goo.gl/SqGKuo.

Benchmarking of cell type deconvolution pipelines for transcriptomics data

“…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 .…”

Tumour immune cell infiltration and survival after platinum-based chemotherapy in high-grade serous ovarian cancer subtypes: A gene expression-based computational study

“…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.…”

CTLA4 has a profound impact on the landscape of tumor-infiltrating lymphocytes with a high prognosis value in clear cell renal cell carcinoma (ccRCC)

“…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).…”