imply: improving cell-type deconvolution accuracy using personalized reference profiles

Guanqun Meng; Yue Pan; Wen Tang; Lijun Zhang; Ying Cui; Fredrick R Schumacher; Ming Wang; Rui Wang; Sijia He; Jeffrey Krischer; Qian Li; Hao Feng

doi:10.1186/s13073-024-01338-z

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Genome Med. 2024 Apr 29;16(1):65. doi: 10.1186/s13073-024-01338-z.

Authors

Guanqun Meng¹, Yue Pan², Wen Tang¹, Lijun Zhang¹, Ying Cui³, Fredrick R Schumacher¹, Ming Wang¹, Rui Wang⁴, Sijia He⁵, Jeffrey Krischer⁶, Qian Li⁷, Hao Feng⁸

Affiliations

¹ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA.
² Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA.
³ Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA.
⁴ Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA.
⁵ Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA.
⁶ Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA.
⁷ Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA. Qian.Li@stjude.org.
⁸ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA. hxf155@case.edu.

Abstract

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson's disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

Keywords: Admixed samples; Bulk RNA-seq; Cell-type-specific; Deconvolution; Personalized reference.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Algorithms*
Computational Biology / methods
Diabetes Mellitus, Type 1 / genetics
Gene Expression Profiling / methods
Humans
Parkinson Disease* / genetics
Precision Medicine / methods
Software
Transcriptome

Abstract

Publication types

MeSH terms

Grants and funding