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Bakulin A, Teyssier NB, Kampmann M, Khoroshkin M, Goodarzi H. pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease. PLoS Comput Biol 2024; 20:e1012346. [PMID: 39236079 DOI: 10.1371/journal.pcbi.1012346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 07/22/2024] [Indexed: 09/07/2024] Open
Abstract
Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease.
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Affiliation(s)
- Artemy Bakulin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Noam B Teyssier
- Institute for Neurodegenerative Diseases, University of California San Francisco, California, United States of America
| | - Martin Kampmann
- Institute for Neurodegenerative Diseases, University of California San Francisco, California, United States of America
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
| | - Matvei Khoroshkin
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Arc Institute, Palo Alto, California, United States of America
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2
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Maulding ND, Seninge L, Stuart JM. Associating transcription factors to single-cell trajectories with DREAMIT. Genome Biol 2024; 25:220. [PMID: 39143494 PMCID: PMC11323358 DOI: 10.1186/s13059-024-03368-7] [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: 05/26/2023] [Accepted: 08/06/2024] [Indexed: 08/16/2024] Open
Abstract
Inferring gene regulatory networks from single-cell RNA-sequencing trajectories has been an active area of research yet methods are still needed to identify regulators governing cell transitions. We developed DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories) to annotate transcription-factor activity along single-cell trajectory branches, using ensembles of relations to target genes. Using a benchmark representing several different tissues, as well as external validation with ATAC-Seq and Perturb-Seq data on hematopoietic cells, the method was found to have higher tissue-specific sensitivity and specificity over competing approaches.
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Affiliation(s)
- Nathan D Maulding
- UCSC Genomics Institute, Biomolecular Engineering, University of California, Santa Cruz, USA
| | - Lucas Seninge
- UCSC Genomics Institute, Biomolecular Engineering, University of California, Santa Cruz, USA
| | - Joshua M Stuart
- UCSC Genomics Institute, Biomolecular Engineering, University of California, Santa Cruz, USA.
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3
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Hecker D, Lauber M, Behjati Ardakani F, Ashrafiyan S, Manz Q, Kersting J, Hoffmann M, Schulz MH, List M. Computational tools for inferring transcription factor activity. Proteomics 2023; 23:e2200462. [PMID: 37706624 DOI: 10.1002/pmic.202200462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/11/2023] [Accepted: 08/22/2023] [Indexed: 09/15/2023]
Abstract
Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.
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Affiliation(s)
- Dennis Hecker
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Michael Lauber
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Fatemeh Behjati Ardakani
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Shamim Ashrafiyan
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Quirin Manz
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Johannes Kersting
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- GeneSurge GmbH, München, Germany
| | - Markus Hoffmann
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Garching, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Marcel H Schulz
- Goethe University Frankfurt, Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner site Rhein-Main, Frankfurt am Main, Germany
- Cardio-Pulmonary Institute, Goethe University Hospital, Frankfurt am Main, Germany
| | - Markus List
- Big Data in BioMedicine Group, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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Gong M, He Y, Wang M, Zhang Y, Ding C. Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism. Comput Biol Chem 2023; 106:107923. [PMID: 37598467 DOI: 10.1016/j.compbiolchem.2023.107923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/01/2023] [Accepted: 07/12/2023] [Indexed: 08/22/2023]
Abstract
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts the convolution neural network to extract the data feature and optimize the pre-trained model. In particular, the model obtains faster training and prediction due to the embedded attention mechanism. For datasets, we take ATAC-seq, ChIP-seq, and DNA sequences data for the pre-trained model, and single-cell ATAC-seq data is used to predict the TF binding graph in the given cell. We verify the interpretability of the model through ablation experiments and sensitivity analysis. IscPAM can efficiently predict the combination of whole genome transcription factors in single cells and study cellular heterogeneity through chromatin accessibility of related diseases.
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Affiliation(s)
- Meiqin Gong
- West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yuchen He
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Maocheng Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
| | - Chunli Ding
- Sichuan Institute of Computer Sciences, Chengdu 610041, China.
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Hecker D, Behjati Ardakani F, Karollus A, Gagneur J, Schulz MH. The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data. Bioinformatics 2023; 39:btad062. [PMID: 36708003 PMCID: PMC9931646 DOI: 10.1093/bioinformatics/btad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/05/2022] [Accepted: 01/26/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a gene's perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is neither implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data. RESULTS We demonstrate that the ABC score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalized ABC model with TF binding information and illustrated an analysis of a single-cell ATAC-seq dataset of the human heart, where we were able to characterize cell type-specific regulatory interactions and predict gene expression based on TF affinities. All executed processing steps are incorporated into our new computational pipeline STARE. AVAILABILITY AND IMPLEMENTATION The software is available at https://github.com/schulzlab/STARE. CONTACT marcel.schulz@em.uni-frankfurt.de. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dennis Hecker
- Institute of Cardiovascular Regeneration, Goethe University Hospital
- Cardio-Pulmonary Institute, Goethe University
- German Centre for Cardiovascular Research, Partner site Rhine-Main, Frankfurt am Main 60590
| | - Fatemeh Behjati Ardakani
- Institute of Cardiovascular Regeneration, Goethe University Hospital
- Cardio-Pulmonary Institute, Goethe University
- German Centre for Cardiovascular Research, Partner site Rhine-Main, Frankfurt am Main 60590
| | - Alexander Karollus
- School of Computation, Information and Technology, Technical University of Munich, Garching 85748
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching 85748
- Institute of Human Genetics, Technical University of Munich, Munich 81675
- Computational Health Center, Helmholtz Center Munich, Neuherberg 85764
- Munich Data Science Institute, Technical University of Munich, Garching 85748, Germany
| | - Marcel H Schulz
- Institute of Cardiovascular Regeneration, Goethe University Hospital
- Cardio-Pulmonary Institute, Goethe University
- German Centre for Cardiovascular Research, Partner site Rhine-Main, Frankfurt am Main 60590
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Pei J, van den Dungen NAM, Asselbergs FW, Mokry M, Harakalova M. Chromatin Immunoprecipitation Sequencing (ChIP-seq) Protocol for Small Amounts of Frozen Biobanked Cardiac Tissue. Methods Mol Biol 2022; 2458:97-111. [PMID: 35103964 DOI: 10.1007/978-1-0716-2140-0_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Chromatin immunoprecipitation and sequencing (ChIP-seq) is a well-established method to study the epigenetic profile at the genome-wide scale, including histone modifications and DNA-protein interactions. It provides valuable insights to better understand disease mechanisms. Here we present an optimized ChIP-seq protocol suitable for human cardiac tissues, especially the frozen biobanked small biopsy samples.
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Affiliation(s)
- Jiayi Pei
- Department of Cardiology, Division Heart & Lungs, UMC Utrecht, University of Utrecht, Utrecht, The Netherlands
- Regenerative Medicine Utrecht (RMU), UMC Utrecht, University of Utrecht, Utrecht, The Netherlands
| | | | - Folkert W Asselbergs
- Department of Cardiology, Division Heart & Lungs, UMC Utrecht, University of Utrecht, Utrecht, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
| | - Michal Mokry
- Department of Cardiology, Division Heart & Lungs, UMC Utrecht, University of Utrecht, Utrecht, The Netherlands
- Laboratory of Clinical Chemistry and Hematology, UMC Utrecht, Utrecht, The Netherlands
| | - Magdalena Harakalova
- Department of Cardiology, Division Heart & Lungs, UMC Utrecht, University of Utrecht, Utrecht, The Netherlands.
- Regenerative Medicine Utrecht (RMU), UMC Utrecht, University of Utrecht, Utrecht, The Netherlands.
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Sacher F, Feregrino C, Tschopp P, Ewald CY. Extracellular matrix gene expression signatures as cell type and cell state identifiers. Matrix Biol Plus 2021; 10:100069. [PMID: 34195598 PMCID: PMC8233473 DOI: 10.1016/j.mbplus.2021.100069] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/06/2021] [Accepted: 05/06/2021] [Indexed: 02/07/2023] Open
Abstract
Transcriptomic signatures based on cellular mRNA expression profiles can be used to categorize cell types and states. Yet whether different functional groups of genes perform better or worse in this process remains largely unexplored. Here we test the core matrisome - that is, all genes coding for structural proteins of the extracellular matrix - for its ability to delineate distinct cell types in embryonic single-cell RNA-sequencing (scRNA-seq) data. We show that even though expressed core matrisome genes correspond to less than 2% of an entire cellular transcriptome, their RNA expression levels suffice to recapitulate essential aspects of cell type-specific clustering. Notably, using scRNA-seq data from the embryonic limb, we demonstrate that core matrisome gene expression outperforms random gene subsets of similar sizes and can match and exceed the predictive power of transcription factors. While transcription factor signatures generally perform better in predicting cell types at early stages of chicken and mouse limb development, i.e., when cells are less differentiated, the information content of the core matrisome signature increases in more differentiated cells. Moreover, using cross-species analyses, we show that these cell type-specific signatures are evolutionarily conserved. Our findings suggest that each cell type produces its own unique extracellular matrix, or matreotype, which becomes progressively more refined and cell type-specific as embryonic tissues mature.
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Affiliation(s)
- Fabio Sacher
- Laboratory of Regulatory Evolution, DUW Zoology, University of Basel, Basel CH-4051, Switzerland
| | - Christian Feregrino
- Laboratory of Regulatory Evolution, DUW Zoology, University of Basel, Basel CH-4051, Switzerland
| | - Patrick Tschopp
- Laboratory of Regulatory Evolution, DUW Zoology, University of Basel, Basel CH-4051, Switzerland
| | - Collin Y. Ewald
- Laboratory of Extracellular Matrix Regeneration, Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zürich, Schwerzenbach CH-8603, Switzerland
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Adossa N, Khan S, Rytkönen KT, Elo LL. Computational strategies for single-cell multi-omics integration. Comput Struct Biotechnol J 2021; 19:2588-2596. [PMID: 34025945 PMCID: PMC8114078 DOI: 10.1016/j.csbj.2021.04.060] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 02/06/2023] Open
Abstract
Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.
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Affiliation(s)
- Nigatu Adossa
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Sofia Khan
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Kalle T. Rytkönen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland
| | - Laura L. Elo
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
- Institute of Biomedicine, University of Turku, 20520 Turku, Finland
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Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines. Biomolecules 2021; 11:biom11020177. [PMID: 33525507 PMCID: PMC7912277 DOI: 10.3390/biom11020177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 01/21/2021] [Accepted: 01/23/2021] [Indexed: 12/13/2022] Open
Abstract
Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. Several cellular models have been implemented to study this disease over the years. Two of these, SK-N-BE-2-C (BE2C) and Kelly, are amongst the most used worldwide as models of MYCN-Amplified human NBL. Here, we provide a transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution. We obtained 1105 Kelly and 962 BE2C unsynchronized cells, with an average number of mapped reads/cell of roughly 38,000. The single-cell data recapitulate gene expression signatures previously generated from bulk RNA-Seq. We highlight low variance for commonly used housekeeping genes between different cells (ACTB, B2M and GAPDH), while showing higher than expected variance for metallothionein transcripts in Kelly cells. The high number of samples, despite the relatively low read coverage of single cells, allowed for robust pathway enrichment analysis and master regulator analysis (MRA), both of which highlight the more mesenchymal nature of BE2C cells as compared to Kelly cells, and the upregulation of TWIST1 and DNAJC1 transcriptional networks. We further defined master regulators at the single cell level and showed that MYCN is not constantly active or expressed within Kelly and BE2C cells, independently of cell cycle phase. The dataset, alongside a detailed and commented programming protocol to analyze it, is fully shared and reusable.
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Behjati Ardakani F, Kattler K, Heinen T, Schmidt F, Feuerborn D, Gasparoni G, Lepikhov K, Nell P, Hengstler J, Walter J, Schulz MH. Prediction of single-cell gene expression for transcription factor analysis. Gigascience 2020; 9:giaa113. [PMID: 33124660 PMCID: PMC7596801 DOI: 10.1093/gigascience/giaa113] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/20/2020] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. RESULTS Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. CONCLUSION Our proposed method allows us to identify distinct TFs that show cell type-specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.
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Affiliation(s)
- Fatemeh Behjati Ardakani
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Campus E1 3, Saarbrücken, Germany
| | - Kathrin Kattler
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Tobias Heinen
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Florian Schmidt
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Graduate School of Computer Science, Saarland University, Campus E1 3, Saarbrücken, Germany
| | - David Feuerborn
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Gilles Gasparoni
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Konstantin Lepikhov
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Patrick Nell
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Jan Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Ardeystraße 67, 44139 Dortmund, Germany
| | - Jörn Walter
- Department of Genetics, Saarland University, Campus A2 4, 66123 Saarbrücken, Germany
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany; Theodor-Stern-Kai 7
- Cluster of Excellence MMCI, Saarland University, Campus E1 7, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Max Planck Institute for Informatics, Campus E1 4, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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