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Al Kamran Khan MA, Wu J, Sun Y, Barrow AD, Papenfuss AT, Mangiola S. cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling. Bioinformatics 2023; 39:btad685. [PMID: 37952182 PMCID: PMC10692870 DOI: 10.1093/bioinformatics/btad685] [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: 02/11/2023] [Revised: 09/19/2023] [Accepted: 11/10/2023] [Indexed: 11/14/2023] Open
Abstract
MOTIVATION The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation. RESULTS Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks. AVAILABILITY AND IMPLEMENTATION Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.
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Affiliation(s)
- Md Abdullah Al Kamran Khan
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia
| | - Jian Wu
- Cancer Biology And Therapy, Olivia Newton-John Cancer Research Institute, Heidelberg, VIC 3038, Australia
| | - Yuhan Sun
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia
| | - Alexander D Barrow
- Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, VIC 3010, Australia
| | - Anthony T Papenfuss
- Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3010, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Stefano Mangiola
- Division of Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3010, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, VIC 3010, Australia
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Mangiola S, Roth-Schulze AJ, Trussart M, Zozaya-Valdés E, Ma M, Gao Z, Rubin AF, Speed TP, Shim H, Papenfuss AT. sccomp: Robust differential composition and variability analysis for single-cell data. Proc Natl Acad Sci U S A 2023; 120:e2203828120. [PMID: 37549298 PMCID: PMC10438834 DOI: 10.1073/pnas.2203828120] [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: 03/05/2022] [Accepted: 05/18/2023] [Indexed: 08/09/2023] Open
Abstract
Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.
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Affiliation(s)
- Stefano Mangiola
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Alexandra J. Roth-Schulze
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Marie Trussart
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
| | - Enrique Zozaya-Valdés
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Mengyao Ma
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
| | - Zijie Gao
- Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC3052, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC3052, Australia
| | - Alan F. Rubin
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
| | - Terence P. Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
| | - Heejung Shim
- Melbourne Integrative Genomics, University of Melbourne, Parkville, VIC3052, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, VIC3052, Australia
| | - Anthony T. Papenfuss
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC3052, Australia
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Singh P, Gollapalli K, Mangiola S, Schranner D, Yusuf MA, Chamoli M, Shi SL, Bastos BL, Nair T, Riermeier A, Vayndorf EM, Wu JZ, Nilakhe A, Nguyen CQ, Muir M, Kiflezghi MG, Foulger A, Junker A, Devine J, Sharan K, Chinta SJ, Rajput S, Rane A, Baumert P, Schönfelder M, Iavarone F, Lorenzo GD, Kumari S, Gupta A, Sarkar R, Khyriem C, Chawla AS, Sharma A, Sarper N, Chattopadhyay N, Biswal BK, Settembre C, Nagarajan P, Targoff KL, Picard M, Gupta S, Velagapudi V, Papenfuss AT, Kaya A, Ferreira MG, Kennedy BK, Andersen JK, Lithgow GJ, Ali AM, Mukhopadhyay A, Palotie A, Kastenmüller G, Kaeberlein M, Wackerhage H, Pal B, Yadav VK. Taurine deficiency as a driver of aging. Science 2023; 380:eabn9257. [PMID: 37289866 PMCID: PMC10630957 DOI: 10.1126/science.abn9257] [Citation(s) in RCA: 64] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Accepted: 04/14/2023] [Indexed: 06/10/2023]
Abstract
Aging is associated with changes in circulating levels of various molecules, some of which remain undefined. We find that concentrations of circulating taurine decline with aging in mice, monkeys, and humans. A reversal of this decline through taurine supplementation increased the health span (the period of healthy living) and life span in mice and health span in monkeys. Mechanistically, taurine reduced cellular senescence, protected against telomerase deficiency, suppressed mitochondrial dysfunction, decreased DNA damage, and attenuated inflammaging. In humans, lower taurine concentrations correlated with several age-related diseases and taurine concentrations increased after acute endurance exercise. Thus, taurine deficiency may be a driver of aging because its reversal increases health span in worms, rodents, and primates and life span in worms and rodents. Clinical trials in humans seem warranted to test whether taurine deficiency might drive aging in humans.
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Affiliation(s)
- Parminder Singh
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Kishore Gollapalli
- Vagelos College of Physicians and Surgeons, Columbia University; New York, USA
| | - Stefano Mangiola
- Department of Medical Biology, University of Melbourne; Melbourne, Australia
- School of Cancer Medicine, La Trobe University; Bundoora, Australia
- Olivia Newton-John Cancer Research Institute; Heidelberg, Australia
| | - Daniela Schranner
- Exercise Biology Group, Technical University of Munich; Munich, Germany
- Institute of Computational Biology, Helmholtz Zentrum München; Neuherberg, Germany
| | - Mohd Aslam Yusuf
- Department of Bioengineering, Integral University; Lucknow, India
| | - Manish Chamoli
- Buck Institute of Age Research, 8001 Redwood Blvd; California, USA
| | - Sting L. Shi
- Vagelos College of Physicians and Surgeons, Columbia University; New York, USA
| | - Bruno Lopes Bastos
- Institute for Research on Cancer and Aging of Nice (IRCAN); Nice, France
| | - Tripti Nair
- Molecular Aging Laboratory, National Institute of Immunology; New Delhi, India
| | - Annett Riermeier
- Exercise Biology Group, Technical University of Munich; Munich, Germany
| | - Elena M. Vayndorf
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Judy Z. Wu
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Aishwarya Nilakhe
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Christina Q. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Michael Muir
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Michael G. Kiflezghi
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | - Anna Foulger
- Buck Institute of Age Research, 8001 Redwood Blvd; California, USA
| | - Alex Junker
- Department of Neurology, Columbia University; New York, USA
| | - Jack Devine
- Department of Neurology, Columbia University; New York, USA
| | - Kunal Sharan
- Mouse Genetics Project, Wellcome Sanger Institute; Cambridge, UK
| | | | - Swati Rajput
- Division of Endocrinology, CSIR-Central Drug Research Institute; Lucknow, India
| | - Anand Rane
- Buck Institute of Age Research, 8001 Redwood Blvd; California, USA
| | - Philipp Baumert
- Exercise Biology Group, Technical University of Munich; Munich, Germany
| | | | | | | | - Swati Kumari
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Alka Gupta
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Rajesh Sarkar
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Costerwell Khyriem
- Harry Perkins Institute of Medical Research; Perth, Australia
- Curtin Medical School, Curtin University; Perth, Australia
| | - Amanpreet S. Chawla
- Immunobiology Laboratory, National Institute of Immunology; New Delhi, India
- MRC-Protein Phosphorylation and Ubiquitination Unit, University of Dundee; Dundee, UK
| | - Ankur Sharma
- Harry Perkins Institute of Medical Research; Perth, Australia
- Curtin Medical School, Curtin University; Perth, Australia
| | - Nazan Sarper
- Pediatrics and Pediatric Hematology, Kocaeli University Hospital; Kocaeli, Turkey
| | | | - Bichitra K. Biswal
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Carmine Settembre
- Telethon Institute of Genetics and Medicine (TIGEM); Pozzuoli, Italy
- Department of Clinical Medicine and Surgery, Federico II University; Naples, Italy
| | - Perumal Nagarajan
- Primate Research Facility, National Institute of Immunology; New Delhi, India
- Small Animal Research Facility, National Institute of Immunology; New Delhi, India
| | - Kimara L. Targoff
- Division of Cardiology, Department of Pediatrics, Columbia University; New York, USA
| | - Martin Picard
- Department of Neurology, Columbia University; New York, USA
| | - Sarika Gupta
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
| | - Vidya Velagapudi
- Institute for Molecular Medicine Finland FIMM, University of Helsinki; Helsinki, Finland
| | | | - Alaattin Kaya
- Department of Biology, Virginia Commonwealth University; Virginia, USA
| | | | - Brian K. Kennedy
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore; Singapore, Singapore
- Centre for Healthy Longevity, National University Health System; Singapore, Singapore
- Departments of Biochemistry and Physiology, Yong Loo Lin School of Medicine, National University of Singapore; Singapore, Singapore
| | | | | | - Abdullah Mahmood Ali
- Department of Medicine, Columbia University Irving Medical Center; New York, USA
| | - Arnab Mukhopadhyay
- Molecular Aging Laboratory, National Institute of Immunology; New Delhi, India
| | - Aarno Palotie
- Institute for Molecular Medicine Finland FIMM, University of Helsinki; Helsinki, Finland
- Broad Institute of Harvard and MIT; Cambridge, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital; Boston, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München; Neuherberg, Germany
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, University of Washington; WA, USA
| | | | - Bhupinder Pal
- Department of Medical Biology, University of Melbourne; Melbourne, Australia
- School of Cancer Medicine, La Trobe University; Bundoora, Australia
| | - Vijay K. Yadav
- Metabolic Research Laboratories, National Institute of Immunology; New Delhi, India
- Vagelos College of Physicians and Surgeons, Columbia University; New York, USA
- Mouse Genetics Project, Wellcome Sanger Institute; Cambridge, UK
- Department of Genetics and Development, Columbia University; New York, USA
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Parkinson E, Liberatore F, Watkins WJ, Andrews R, Edkins S, Hibbert J, Strunk T, Currie A, Ghazal P. Gene filtering strategies for machine learning guided biomarker discovery using neonatal sepsis RNA-seq data. Front Genet 2023; 14:1158352. [PMID: 37113992 PMCID: PMC10126415 DOI: 10.3389/fgene.2023.1158352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/29/2023] [Indexed: 04/29/2023] Open
Abstract
Machine learning (ML) algorithms are powerful tools that are increasingly being used for sepsis biomarker discovery in RNA-Seq data. RNA-Seq datasets contain multiple sources and types of noise (operator, technical and non-systematic) that may bias ML classification. Normalisation and independent gene filtering approaches described in RNA-Seq workflows account for some of this variability and are typically only targeted at differential expression analysis rather than ML applications. Pre-processing normalisation steps significantly reduce the number of variables in the data and thereby increase the power of statistical testing, but can potentially discard valuable and insightful classification features. A systematic assessment of applying transcript level filtering on the robustness and stability of ML based RNA-seq classification remains to be fully explored. In this report we examine the impact of filtering out low count transcripts and those with influential outliers read counts on downstream ML analysis for sepsis biomarker discovery using elastic net regularised logistic regression, L1-reguarlised support vector machines and random forests. We demonstrate that applying a systematic objective strategy for removal of uninformative and potentially biasing biomarkers representing up to 60% of transcripts in different sample size datasets, including two illustrative neonatal sepsis cohorts, leads to substantial improvements in classification performance, higher stability of the resulting gene signatures, and better agreement with previously reported sepsis biomarkers. We also demonstrate that the performance uplift from gene filtering depends on the ML classifier chosen, with L1-regularlised support vector machines showing the greatest performance improvements with our experimental data.
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Affiliation(s)
- Edward Parkinson
- Department of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
- *Correspondence: Edward Parkinson,
| | - Federico Liberatore
- Department of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - W. John Watkins
- Project Sepsis, Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Robert Andrews
- Project Sepsis, Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Sarah Edkins
- Project Sepsis, Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Julie Hibbert
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, WA, Australia
- Medical School, University of Western Australia, Perth, WA, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA, Australia
| | - Tobias Strunk
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, WA, Australia
- Medical School, University of Western Australia, Perth, WA, Australia
- Neonatal Directorate, Child and Adolescent Health Service, Perth, WA, Australia
| | - Andrew Currie
- Wesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Perth, WA, Australia
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Perth, WA, Australia
| | - Peter Ghazal
- Project Sepsis, Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom
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Hillerton T, Seçilmiş D, Nelander S, Sonnhammer ELL. Fast and accurate gene regulatory network inference by normalized least squares regression. Bioinformatics 2022; 38:2263-2268. [PMID: 35176145 PMCID: PMC9004640 DOI: 10.1093/bioinformatics/btac103] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 01/10/2022] [Accepted: 02/15/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Inferring an accurate gene regulatory network (GRN) has long been a key goal in the field of systems biology. To do this, it is important to find a suitable balance between the maximum number of true positive and the minimum number of false-positive interactions. Another key feature is that the inference method can handle the large size of modern experimental data, meaning the method needs to be both fast and accurate. The Least Squares Cut-Off (LSCO) method can fulfill both these criteria, however as it is based on least squares it is vulnerable to known issues of amplifying extreme values, small or large. In GRN this manifests itself with genes that are erroneously hyper-connected to a large fraction of all genes due to extremely low value fold changes. RESULTS We developed a GRN inference method called Least Squares Cut-Off with Normalization (LSCON) that tackles this problem. LSCON extends the LSCO algorithm by regularization to avoid hyper-connected genes and thereby reduce false positives. The regularization used is based on normalization, which removes effects of extreme values on the fit. We benchmarked LSCON and compared it to Genie3, LASSO, LSCO and Ridge regression, in terms of accuracy, speed and tendency to predict hyper-connected genes. The results show that LSCON achieves better or equal accuracy compared to LASSO, the best existing method, especially for data with extreme values. Thanks to the speed of least squares regression, LSCON does this an order of magnitude faster than LASSO. AVAILABILITY AND IMPLEMENTATION Data: https://bitbucket.org/sonnhammergrni/lscon; Code: https://bitbucket.org/sonnhammergrni/genespider. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Thomas Hillerton
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, 17121 Solna, Sweden
| | - Deniz Seçilmiş
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, 17121 Solna, Sweden
| | - Sven Nelander
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, 75185 Uppsala, Sweden
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