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Gregory W, Sarwar N, Kevrekidis G, Villar S, Dumitrascu B. MarkerMap: nonlinear marker selection for single-cell studies. NPJ Syst Biol Appl 2024; 10:17. [PMID: 38351188 PMCID: PMC10864304 DOI: 10.1038/s41540-024-00339-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 01/17/2024] [Indexed: 02/16/2024] Open
Abstract
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap's competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.
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Affiliation(s)
- Wilson Gregory
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Nabeel Sarwar
- Center for Data Science, New York University, New York, NY, 10012, USA
| | - George Kevrekidis
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Soledad Villar
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Bianca Dumitrascu
- Department of Statistics, Columbia University, New York, NY, 10027, USA.
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, 10027, USA.
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Viñas R, Joshi CK, Georgiev D, Lin P, Dumitrascu B, Gamazon ER, Liò P. Hypergraph factorization for multi-tissue gene expression imputation. NAT MACH INTELL 2023; 5:739-753. [PMID: 37771758 PMCID: PMC10538467 DOI: 10.1038/s42256-023-00684-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 06/02/2023] [Indexed: 09/30/2023]
Abstract
Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.
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Affiliation(s)
- Ramon Viñas
- Department of Computer Science and Technology, University of Cambridge
| | | | - Dobrik Georgiev
- Department of Computer Science and Technology, University of Cambridge
| | - Phillip Lin
- Division of Genetic Medicine, Vanderbilt University Medical Center
| | - Bianca Dumitrascu
- Department of Statistics and Irving Institute for Cancer Dynamics, Columbia University
| | - Eric R. Gamazon
- Vanderbilt Genetics Institute and Data Science Institute, MRC Epidemiology Unit, University of Cambridge
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge
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Abstract
Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.
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Affiliation(s)
- Adrien Hallou
- Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK
- Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, CB2 1QR, UK
| | - Hannah G. Yevick
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA
| | - Bianca Dumitrascu
- Computer Laboratory, Cambridge, University of Cambridge, Cambridge, CB3 0FD, UK
| | - Virginie Uhlmann
- European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK
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Dumitrascu B, Villar S, Mixon DG, Engelhardt BE. Optimal marker gene selection for cell type discrimination in single cell analyses. Nat Commun 2021; 12:1186. [PMID: 33608535 PMCID: PMC7895823 DOI: 10.1038/s41467-021-21453-4] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 01/27/2021] [Indexed: 11/17/2022] Open
Abstract
Single-cell technologies characterize complex cell populations across multiple data modalities at unprecedented scale and resolution. Multi-omic data for single cell gene expression, in situ hybridization, or single cell chromatin states are increasingly available across diverse tissue types. When isolating specific cell types from a sample of disassociated cells or performing in situ sequencing in collections of heterogeneous cells, one challenging task is to select a small set of informative markers that robustly enable the identification and discrimination of specific cell types or cell states as precisely as possible. Given single cell RNA-seq data and a set of cellular labels to discriminate, scGeneFit selects gene markers that jointly optimize cell label recovery using label-aware compressive classification methods. This results in a substantially more robust and less redundant set of markers than existing methods, most of which identify markers that separate each cell label from the rest. When applied to a data set given a hierarchy of cell types as labels, the markers found by our method improves the recovery of the cell type hierarchy with fewer markers than existing methods using a computationally efficient and principled optimization. The selection of a small set of cellular labels to distinguish a subpopulation of cells from a complex mixture is an important task in cell biology. Here the authors propose a method for supervised genetic marker selection using linear programming and provides a Python package scGeneFit that implements this approach.
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Affiliation(s)
- Bianca Dumitrascu
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Soledad Villar
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.,Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Dustin G Mixon
- Department of Mathematics, The Ohio State University, Columbus, OH, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ, USA. .,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA.
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Lu J, Dumitrascu B, McDowell IC, Jo B, Barrera A, Hong LK, Leichter SM, Reddy TE, Engelhardt BE. Causal network inference from gene transcriptional time-series response to glucocorticoids. PLoS Comput Biol 2021; 17:e1008223. [PMID: 33513136 PMCID: PMC7875426 DOI: 10.1371/journal.pcbi.1008223] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 02/10/2021] [Accepted: 08/07/2020] [Indexed: 11/19/2022] Open
Abstract
Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.
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Affiliation(s)
- Jonathan Lu
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Ian C. McDowell
- Element Genomics, A UCB Company, Durham, North Carolina, United States of America
| | - Brian Jo
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, United States of America
| | - Alejandro Barrera
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, United States of America
| | - Linda K. Hong
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Sarah M. Leichter
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Timothy E. Reddy
- Department of Genome Sciences, Duke University, Durham, North Carolina, United States of America
| | - Barbara E. Engelhardt
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, United States of America
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Camerlenghi F, Dumitrascu B, Ferrari F, Engelhardt BE, Favaro S. Nonparametric Bayesian multiarmed bandits for single-cell experiment design. Ann Appl Stat 2020. [DOI: 10.1214/20-aoas1370] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Cheng LF, Dumitrascu B, Darnell G, Chivers C, Draugelis M, Li K, Engelhardt BE. Sparse multi-output Gaussian processes for online medical time series prediction. BMC Med Inform Decis Mak 2020; 20:152. [PMID: 32641134 PMCID: PMC7341595 DOI: 10.1186/s12911-020-1069-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 03/05/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND For real-time monitoring of hospital patients, high-quality inference of patients' health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on multi-output Gaussian process (GP) regression for hospital patient monitoring. METHODS We propose MedGP, a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time. To do this, we develop a highly structured sparse GP kernel to enable tractable computation over tens of thousands of time points while estimating correlations among clinical covariates, patients, and periodicity in patient observations. MedGP has a number of benefits over current methods, including (i) not requiring an alignment of the time series data, (ii) quantifying confidence regions in the predictions, (iii) exploiting a vast and rich database of patients, and (iv) inferring interpretable relationships among clinical covariates. RESULTS We evaluate and compare results from MedGP on the task of online prediction for three patient subgroups from two medical data sets across 8,043 patients. We find MedGP improves online prediction over baseline and state-of-the-art methods for nearly all covariates across different disease subgroups and hospitals. CONCLUSIONS The MedGP framework is robust and efficient in estimating the temporal dependencies from sparse and irregularly sampled medical time series data for online prediction. The publicly available code is at https://github.com/bee-hive/MedGP .
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Affiliation(s)
- Li-Fang Cheng
- Department of Electrical Engineering, Princeton University, Princeton, USA
| | | | - Gregory Darnell
- Lewis-Sigler Institute, Princeton University, Princeton, NJ USA
| | - Corey Chivers
- University of Pennsylvania Health System, Philadelphia, PA USA
| | | | - Kai Li
- Department of Computer Science, Princeton University, Princeton, NJ USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ USA
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8
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Elyanow R, Dumitrascu B, Engelhardt BE, Raphael BJ. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis. Genome Res 2020; 30:195-204. [PMID: 31992614 PMCID: PMC7050525 DOI: 10.1101/gr.251603.119] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023]
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene–gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene–gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains.
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Affiliation(s)
- Rebecca Elyanow
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island 02912, USA.,Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08540, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA.,Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
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Dumitrascu B, Darnell G, Ayroles J, Engelhardt BE. Statistical tests for detecting variance effects in quantitative trait studies. Bioinformatics 2019; 35:200-210. [PMID: 29982387 PMCID: PMC6330007 DOI: 10.1093/bioinformatics/bty565] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Accepted: 07/04/2018] [Indexed: 11/17/2022] Open
Abstract
Motivation Identifying variants, both discrete and continuous, that are associated with quantitative traits, or QTs, is the primary focus of quantitative genetics. Most current methods are limited to identifying mean effects, or associations between genotype or covariates and the mean value of a quantitative trait. It is possible, however, that a variant may affect the variance of the quantitative trait in lieu of, or in addition to, affecting the trait mean. Here, we develop a general methodology to identify covariates with variance effects on a quantitative trait using a Bayesian heteroskedastic linear regression model (BTH). We compare BTH with existing methods to detect variance effects across a large range of simulations drawn from scenarios common to the analysis of quantitative traits. Results We find that BTH and a double generalized linear model (dglm) outperform classical tests used for detecting variance effects in recent genomic studies. We show BTH and dglm are less likely to generate spurious discoveries through simulations and application to identifying methylation variance QTs and expression variance QTs. We identify four variance effects of sex in the Cardiovascular and Pharmacogenetics study. Our work is the first to offer a comprehensive view of variance identifying methodology. We identify shortcomings in previously used methodology and provide a more conservative and robust alternative. We extend variance effect analysis to a wide array of covariates that enables a new statistical dimension in the study of sex and age specific quantitative trait effects. Availability and implementation https://github.com/b2du/bth. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Gregory Darnell
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Julien Ayroles
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ, USA.,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
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Aguiar D, Cheng LF, Dumitrascu B, Mordelet F, Pai AA, Engelhardt BE. Bayesian nonparametric discovery of isoforms and individual specific quantification. Nat Commun 2018; 9:1681. [PMID: 29703885 PMCID: PMC5923247 DOI: 10.1038/s41467-018-03402-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 02/11/2018] [Indexed: 12/18/2022] Open
Abstract
Most human protein-coding genes can be transcribed into multiple distinct mRNA isoforms. These alternative splicing patterns encourage molecular diversity, and dysregulation of isoform expression plays an important role in disease etiology. However, isoforms are difficult to characterize from short-read RNA-seq data because they share identical subsequences and occur in different frequencies across tissues and samples. Here, we develop biisq, a Bayesian nonparametric model for isoform discovery and individual specific quantification from short-read RNA-seq data. biisq does not require isoform reference sequences but instead estimates an isoform catalog shared across samples. We use stochastic variational inference for efficient posterior estimates and demonstrate superior precision and recall for simulations compared to state-of-the-art isoform reconstruction methods. biisq shows the most gains for low abundance isoforms, with 36% more isoforms correctly inferred at low coverage versus a multi-sample method and 170% more versus single-sample methods. We estimate isoforms in the GEUVADIS RNA-seq data and validate inferred isoforms by associating genetic variants with isoform ratios. Alternative splicing leads to transcript isoform diversity. Here, Aguiar et al. develop biisq, a Bayesian nonparametric approach to discover and quantify isoforms from RNA-seq data.
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Affiliation(s)
- Derek Aguiar
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
| | - Li-Fang Cheng
- Department of Electrical Engineering, Princeton University, Princeton, NJ, 08540, USA
| | - Bianca Dumitrascu
- Lewis-Sigler Institute, Princeton University, Princeton, NJ, 08544, USA
| | - Fantine Mordelet
- Institute for Genome Sciences and Policy, Duke University, Durham, NC, 27708, USA
| | - Athma A Pai
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA, 01605, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA. .,Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, 08540, USA.
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