imply : improving cell-type deconvolution accuracy using personalized reference profiles
Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that t...
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| Published in | bioRxiv |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article Paper |
| Language | English |
| Published |
United States
Cold Spring Harbor Laboratory
29.09.2023
|
| Edition | 1.1 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2692-8205 2692-8205 |
| DOI | 10.1101/2023.09.27.559579 |
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| Abstract | Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for 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, which ignores person-to-person heterogeneity. Here we present
, a novel algorithm to deconvolute cell type proportions using personalized reference panels.
can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool
is available through the R/Bioconductor package
at https://bioconductor.org/packages/ISLET/. |
|---|---|
| AbstractList | Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for 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, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkin-son’s disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/. Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for 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, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for 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, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/. Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for 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, which ignores person-to-person heterogeneity. Here we present , a novel algorithm to deconvolute cell type proportions using personalized reference panels. can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkinson's disease. Our proposed tool is available through the R/Bioconductor package at https://bioconductor.org/packages/ISLET/. |
| Author | Li, Qian Schumacher, Fredrick R Pan, Yue Feng, Hao Tang, Wen Cui, Ying Zhang, Lijun He, Sijia Meng, Guanqun Wang, Ming Wang, Rui Krischer, Jeffrey |
| Author_xml | – sequence: 1 givenname: Guanqun surname: Meng fullname: Meng, Guanqun organization: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA – sequence: 2 givenname: Yue surname: Pan fullname: Pan, Yue organization: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA – sequence: 3 givenname: Wen surname: Tang fullname: Tang, Wen organization: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA – sequence: 4 givenname: Lijun surname: Zhang fullname: Zhang, Lijun organization: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA – sequence: 5 givenname: Ying surname: Cui fullname: Cui, Ying organization: Department of Biomedical Data Science, Stanford University, Stanford, 94305, CA, USA – sequence: 6 givenname: Fredrick R surname: Schumacher fullname: Schumacher, Fredrick R organization: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA – sequence: 7 givenname: Ming surname: Wang fullname: Wang, Ming organization: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA – sequence: 8 givenname: Rui surname: Wang fullname: Wang, Rui organization: Department of Surgery, Division of Surgical Oncology, University Hospitals Cleveland Medical Center, Cleveland, 44106, OH, USA – sequence: 9 givenname: Sijia surname: He fullname: He, Sijia organization: Department of Biostatistics, University of Michigan, Ann Arbor, 48109, MI, USA – sequence: 10 givenname: Jeffrey surname: Krischer fullname: Krischer, Jeffrey organization: Health Informatics Institute, University of South Florida, Tampa, 38105, FL, USA – sequence: 11 givenname: Qian surname: Li fullname: Li, Qian organization: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, 38105, TN, USA – sequence: 12 givenname: Hao orcidid: 0000-0003-2243-9949 surname: Feng fullname: Feng, Hao organization: Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, 44106, OH, USA |
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