1
|
Liu J, Wang H, Sun W, Liu Y. Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data. J Am Stat Assoc 2022; 117:38-51. [PMID: 35529781 PMCID: PMC9070996 DOI: 10.1080/01621459.2021.1933495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene-gene interaction graphs. We estimate such personalized graphs using single cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1,241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1,500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene's relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies.
Collapse
Affiliation(s)
- Jianyu Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill
| | - Haodong Wang
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill
| | - Wei Sun
- Biostatistics Program, Public Health Sciences Division Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Yufeng Liu
- Department of Statistics and Operations Research, University of North Carolina, Chapel Hill,Department of Genetics, Department of Biostatistics, Carolina Center for Genome Science, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill,
| |
Collapse
|
2
|
Nguyen H, Tran D, Tran B, Pehlivan B, Nguyen T. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data. Brief Bioinform 2021; 22:bbaa190. [PMID: 34020546 PMCID: PMC8138892 DOI: 10.1093/bib/bbaa190] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 06/19/2020] [Accepted: 07/24/2020] [Indexed: 12/13/2022] Open
Abstract
Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development.
Collapse
Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Duc Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bang Tran
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Bahadir Pehlivan
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| | - Tin Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
| |
Collapse
|
3
|
Tritschler S, Büttner M, Fischer DS, Lange M, Bergen V, Lickert H, Theis FJ. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 2019; 146. [DOI: 10.1242/dev.170506] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
Abstract
ABSTRACT
Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.
Collapse
Affiliation(s)
- Sophie Tritschler
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85353 Freising, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - David S. Fischer
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85353 Freising, Germany
| | - Marius Lange
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Volker Bergen
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- German Center for Diabetes Research, 85764 Neuherberg, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- Department of Mathematics, Technische Universität München, 85748 Garching, Germany
| |
Collapse
|
4
|
Bonnaffoux A, Herbach U, Richard A, Guillemin A, Gonin-Giraud S, Gros PA, Gandrillon O. WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinformatics 2019; 20:220. [PMID: 31046682 PMCID: PMC6498543 DOI: 10.1186/s12859-019-2798-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 04/09/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. RESULTS In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. CONCLUSIONS Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.
Collapse
Affiliation(s)
- Arnaud Bonnaffoux
- University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France
- Cosmotech, Lyon, France
| | - Ulysse Herbach
- University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France
- Univ Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5208, Institut Camille Jordan, Villeurbanne, France
| | - Angélique Richard
- University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France
| | - Anissa Guillemin
- University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France
| | - Sandrine Gonin-Giraud
- University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France
| | | | - Olivier Gandrillon
- University Lyon, ENS de Lyon, University Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, Lyon, France
- Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, Lyon, France
| |
Collapse
|
5
|
Abstract
Advances in single-cell transcriptome profiling have contributed to new insights into the cellular states and underlying regulatory networks that govern lineage commitment. Such cell states include multipotent progenitors that can manifest as mixed-lineage patterns of gene expression at a single-cell level. Multipotent and other self-renewing progenitors are often difficult to isolate and characterized by subtle transcriptional differences that are challenging to define. This chapter examines the application of newly developed analytical tools to define heterogeneity in diverse stem cell and multipotent progenitor populations from single-cell RNA-Seq data. In addition to the methodology and output of these approaches, we explore their application to diverse single-cell technologies (e.g., Fluidigm, Drop-Seq, 10× Genomics Chromium) and their usability by computational and non-computational biologists. We focus specifically on the use of one tool, called Iterative Clustering and Guide-gene Selection (ICGS), which has been shown to uncover novel committed, transitional, and metastable progenitor cell states. As a component of the AltAnalyze toolkit, ICGS provides advanced methods to evaluate cellular heterogeneity in combination with regulatory prediction, pathway, and alternative splicing analyses. We walk through the individual steps required to perform these analyses in hematopoietic and embryonic kidney progenitor datasets in both graphical user and command-line interfaces. By the end of this chapter, users should be able to analyze their own single-cell RNA-Seq data and obtain deeper insights into the regulatory biology of the discovered cell states.
Collapse
Affiliation(s)
- Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati School of Medicine, Cincinnati, OH, USA.
| |
Collapse
|
6
|
Hon CC, Shin JW, Carninci P, Stubbington MJT. The Human Cell Atlas: Technical approaches and challenges. Brief Funct Genomics 2018; 17:283-294. [PMID: 29092000 PMCID: PMC6063304 DOI: 10.1093/bfgp/elx029] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
The Human Cell Atlas is a large, international consortium that aims to identify and describe every cell type in the human body. The comprehensive cellular maps that arise from this ambitious effort have the potential to transform many aspects of fundamental biology and clinical practice. Here, we discuss the technical approaches that could be used today to generate such a resource and also the technical challenges that will be encountered.
Collapse
Affiliation(s)
- Chung-Chau Hon
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan
| | - Jay W Shin
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan
| | - Piero Carninci
- RIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan
| | | |
Collapse
|
7
|
Fiers MWEJ, Minnoye L, Aibar S, Bravo González-Blas C, Kalender Atak Z, Aerts S. Mapping gene regulatory networks from single-cell omics data. Brief Funct Genomics 2018; 17:246-254. [PMID: 29342231 PMCID: PMC6063279 DOI: 10.1093/bfgp/elx046] [Citation(s) in RCA: 137] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Single-cell techniques are advancing rapidly and are yielding unprecedented insight into cellular heterogeneity. Mapping the gene regulatory networks (GRNs) underlying cell states provides attractive opportunities to mechanistically understand this heterogeneity. In this review, we discuss recently emerging methods to map GRNs from single-cell transcriptomics data, tackling the challenge of increased noise levels and data sparsity compared with bulk data, alongside increasing data volumes. Next, we discuss how new techniques for single-cell epigenomics, such as single-cell ATAC-seq and single-cell DNA methylation profiling, can be used to decipher gene regulatory programmes. We finally look forward to the application of single-cell multi-omics and perturbation techniques that will likely play important roles for GRN inference in the future.
Collapse
Affiliation(s)
- Mark W E J Fiers
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
| | - Liesbeth Minnoye
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Sara Aibar
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Zeynep Kalender Atak
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| | - Stein Aerts
- VIB Center for Brain & Disease Research, Laboratory of Computational Biology, Leuven, Belgium
- KU Leuven, Department of Human Genetics, Leuven, Belgium
| |
Collapse
|