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Qi G, Battle A. Computational methods for allele-specific expression in single cells. Trends Genet 2024:S0168-9525(24)00169-0. [PMID: 39127549 DOI: 10.1016/j.tig.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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2
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Wu EY, Singh NP, Choi K, Zakeri M, Vincent M, Churchill GA, Ackert-Bicknell CL, Patro R, Love MI. SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty. Genome Biol 2023; 24:165. [PMID: 37438847 PMCID: PMC10337143 DOI: 10.1186/s13059-023-03003-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/29/2023] [Indexed: 07/14/2023] Open
Abstract
Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty. The SEESAW suite of methods is shown to have higher power than other allelic imbalance methods when there is isoform-level allelic imbalance. We also introduce a new test for detecting imbalance that varies across a covariate, such as time.
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Affiliation(s)
- Euphy Y Wu
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - Noor P Singh
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | - Mohsen Zakeri
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | | | | | - Cheryl L Ackert-Bicknell
- Department of Orthopedics, School of Medicine, University of Colorado, Anschutz Campus, Aurora, CO, USA
| | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Michael I Love
- Department of Biostatistics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA.
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3
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Kalita CA, Gusev A. DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk. Genome Biol 2022; 23:152. [PMID: 35804456 PMCID: PMC9264694 DOI: 10.1186/s13059-022-02708-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 06/15/2022] [Indexed: 01/09/2023] Open
Abstract
Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.
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Affiliation(s)
- Cynthia A. Kalita
- grid.38142.3c000000041936754XDivision of Population Sciences, Dana–Farber Cancer Institute & Harvard Medical School, Boston, USA
| | - Alexander Gusev
- grid.38142.3c000000041936754XDivision of Population Sciences, Dana–Farber Cancer Institute & Harvard Medical School, Boston, USA ,grid.66859.340000 0004 0546 1623The Broad Institute, Boston, USA ,grid.62560.370000 0004 0378 8294Division of Genetics, Brigham & Women’s Hospital, Boston, USA
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Mu W, Sarkar H, Srivastava A, Choi K, Patro R, Love MI. Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets. Bioinformatics 2022; 38:2773-2780. [PMID: 35561168 PMCID: PMC9113279 DOI: 10.1093/bioinformatics/btac212] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 03/05/2022] [Accepted: 04/05/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected. RESULTS We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower Root Mean Square Error (RMSE) of allelic ratio estimates than existing methods. In real data, airpart identified differential allelic imbalance patterns across cell states and could be used to define trends of AI signal over spatial or time axes. AVAILABILITY AND IMPLEMENTATION The airpart package is available as an R/Bioconductor package at https://bioconductor.org/packages/airpart. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Wancen Mu
- To whom correspondence should be addressed. or
| | - Hirak Sarkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | | | | | - Rob Patro
- Department of Computer Science, University of Maryland, College Park, MD 20742, USA
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5
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Abplanalp WT, Tucker N, Dimmeler S. Single-cell technologies to decipher cardiovascular diseases. Eur Heart J 2022; 43:4536-4547. [PMID: 35265972 PMCID: PMC9659476 DOI: 10.1093/eurheartj/ehac095] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 01/30/2022] [Accepted: 02/15/2022] [Indexed: 12/14/2022] Open
Abstract
Cardiovascular disease remains the leading cause of death worldwide. A deeper understanding of the multicellular composition and molecular processes may help to identify novel therapeutic strategies. Single-cell technologies such as single-cell or single-nuclei RNA sequencing provide expression profiles of individual cells and allow for dissection of heterogeneity in tissue during health and disease. This review will summarize (i) how these novel technologies have become critical for delineating mechanistic drivers of cardiovascular disease, particularly, in humans and (ii) how they might serve as diagnostic tools for risk stratification or individualized therapy. The review will further discuss technical pitfalls and provide an overview of publicly available human and mouse data sets that can be used as a resource for research.
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Affiliation(s)
- Wesley Tyler Abplanalp
- Institute for Cardiovascular Regeneration, Centre of Molecular Medicine, Goethe University Frankfurt, Theodor Stern Kai 7, 60590 Frankfurt, Germany,German Center for Cardiovascular Research DZHK, Partner site Frankfurt Rhine-Main, Berlin, Germany,Cardiopulmonary Institute, Goethe University Frankfurt, Frankfurt, Germany
| | - Nathan Tucker
- Masonic Medical Research Institute, Utica, NY, USA,Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Stefanie Dimmeler
- Corresponding author. Tel: +49 69 6301 5158, Fax: +49 69 6301 83462,
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Heinen T, Secchia S, Reddington JP, Zhao B, Furlong EEM, Stegle O. scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation. Genome Biol 2022; 23:8. [PMID: 34991671 PMCID: PMC8734213 DOI: 10.1186/s13059-021-02593-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/27/2021] [Indexed: 01/04/2023] Open
Abstract
While it is established that the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies using bulk sequencing either ignore this heterogeneity or use sorted cell populations, reducing discovery and explanatory power. Here, we develop scDALI, a versatile computational framework that integrates information on cellular states with allelic quantifications of single-cell sequencing data to characterize cell-state-specific genetic effects. We apply scDALI to scATAC-seq profiles from developing F1 Drosophila embryos and scRNA-seq from differentiating human iPSCs, uncovering heterogeneous genetic effects in specific lineages, developmental stages, or cell types.
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Affiliation(s)
- Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
| | - Stefano Secchia
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Faculty of Biosciences, Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Heidelberg, Germany
| | - James P Reddington
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Bingqing Zhao
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Eileen E M Furlong
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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Yan Q, Forno E, Celedón JC, Chen W, Weeks DE. CHIT: an allele-specific method for testing the association between molecular quantitative traits and phenotype-genotype interaction. Bioinformatics 2021; 37:4764-4770. [PMID: 34323937 PMCID: PMC8711119 DOI: 10.1093/bioinformatics/btab554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/21/2021] [Accepted: 07/28/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Allele-specific differences in molecular traits can be obtained from next-generation sequencing data and could potentially improve testing power, but such information is usually overlooked in association studies. Furthermore, the variation of molecular quantitative traits (e.g. gene expression) could result from the interaction effect of genotypes and phenotypes, but it is challenging to identify such interaction signals in complex disease studies in humans due to small genetic effect sizes and/or small sample sizes. RESULTS We develop a novel statistical method, the combined haplotype interaction test (CHIT), which tests for association between molecular quantitative traits and phenotype-genotype interactions by modeling the total read counts and allele-specific reads in a target region. CHIT can be used as a supplementary analysis to the regular linear interaction regression. In our simulations, CHIT obtains non-inflated type I error rates, and it has higher power than a standard interaction quantitative trait locus approach based on linear regression models. Finally, we illustrate CHIT by testing associations between gene expression obtained by RNA-seq and the interaction of SNPs and atopy status from a study of childhood asthma in Puerto Ricans, and results demonstrate that CHIT could be more powerful than a standard linear interaction expression quantitative trait loci approach. AVAILABILITY AND IMPLEMENTATION The CHIT algorithm has been implemented in Python. The source code and documentation are available and can be downloaded from https://github.com/QiYanPitt/CHIT. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qi Yan
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Erick Forno
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Juan C Celedón
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Wei Chen
- Division of Pediatric Pulmonary Medicine, UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Auerbach BJ, Hu J, Reilly MP, Li M. Applications of single-cell genomics and computational strategies to study common disease and population-level variation. Genome Res 2021; 31:1728-1741. [PMID: 34599006 PMCID: PMC8494214 DOI: 10.1101/gr.275430.121] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.
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Affiliation(s)
- Benjamin J Auerbach
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
| | - Muredach P Reilly
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York 10032, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania 19104, USA
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