AdRoit is an accurate and robust method to infer complex transcriptome composition

Yang, T, Alessandri-Haber, N, Fury, W, Schaner, M, Breese, R, LaCroix-Fralish, M, Kim, J, Adler, C, Macdonald, LE, Atwal, GS, Bai, Y (October 2021) AdRoit is an accurate and robust method to infer complex transcriptome composition. Communications Biology, 4 (1). ISSN 2399-3642 (In Press)

[img] PDF
2021.Yang.AdRoit.pdf
Available under License Creative Commons Attribution.

Download (4MB)
URL: https://pubmed.ncbi.nlm.nih.gov/34686758/
DOI: 10.1038/s42003-021-02739-1

Abstract

Bulk RNA sequencing provides the opportunity to understand biology at the whole transcriptome level without the prohibitive cost of single cell profiling. Advances in spatial transcriptomics enable to dissect tissue organization and function by genome-wide gene expressions. However, the readout of both technologies is the overall gene expression across potentially many cell types without directly providing the information of cell type constitution. Although several in-silico approaches have been proposed to deconvolute RNA-Seq data composed of multiple cell types, many suffer a deterioration of performance in complex tissues. Here we present AdRoit, an accurate and robust method to infer the cell composition from transcriptome data of mixed cell types. AdRoit uses gene expression profiles obtained from single cell RNA sequencing as a reference. It employs an adaptive learning approach to alleviate the sequencing technique difference between the single cell and the bulk (or spatial) transcriptome data, enhancing cross-platform readout comparability. Our systematic benchmarking and applications, which include deconvoluting complex mixtures that encompass 30 cell types, demonstrate its preferable sensitivity and specificity compared to many existing methods as well as its utilities. In addition, AdRoit is computationally efficient and runs orders of magnitude faster than most methods.

Item Type: Paper
Subjects: bioinformatics > computational biology > algorithms > machine learning
Investigative techniques and equipment > assays > Single cell sequencing
CSHL Authors:
Communities: CSHL labs > Atwal lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 22 October 2021
Date Deposited: 08 Nov 2021 16:23
Last Modified: 08 Nov 2021 16:23
URI: https://repository.cshl.edu/id/eprint/40412

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving