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Chen J, Zhou Y. Weighted Euclidean balancing for a matrix exposure in estimating causal effect. Int J Biostat 2025:ijb-2024-0021. [PMID: 40421507 DOI: 10.1515/ijb-2024-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 04/07/2025] [Indexed: 05/28/2025]
Abstract
With the increasing complexity of data, researchers in various fields have become increasingly interested in estimating the causal effect of a matrix exposure, which involves complex multivariate treatments, on an outcome. Balancing covariates for the matrix exposure is essential to achieve this goal. While exact balancing and approximate balancing methods have been proposed for multiple balancing constraints, dealing with a matrix treatment introduces a large number of constraints, making it challenging to achieve exact balance or select suitable threshold parameters for approximate balancing methods. To address this challenge, the weighted Euclidean balancing method is proposed, which offers an approximate balance of covariates from an overall perspective. In this study, both parametric and nonparametric methods for estimating the causal effect of a matrix treatment is proposed, along with providing theoretical properties of the two estimations. To validate the effectiveness of our approach, extensive simulation results demonstrate that the proposed method outperforms alternative approaches across various scenarios. Finally, we apply the method to analyze the causal impact of the omics variables on the drug sensitivity of Vandetanib. The results indicate that EGFR CNV has a significant positive causal effect on Vandetanib efficacy, whereas EGFR methylation exerts a significant negative causal effect.
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Affiliation(s)
- Juan Chen
- KLATASDS-MOE, School of Statistics, 12655 East China Normal University , 3663 North Zhongshan Road, Shanghai, 200062, P.R. China
- Institute of Brain and Education Innovation, East China Normal University, Shanghai, P.R. China
| | - Yingchun Zhou
- KLATASDS-MOE, School of Statistics, 12655 East China Normal University , 3663 North Zhongshan Road, Shanghai, 200062, P.R. China
- Institute of Brain and Education Innovation, East China Normal University, Shanghai, P.R. China
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2
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Chen J, Min W. sTPLS: identifying common and specific correlated patterns under multiple biological conditions. Brief Bioinform 2025; 26:bbaf195. [PMID: 40285361 PMCID: PMC12031727 DOI: 10.1093/bib/bbaf195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Revised: 03/19/2025] [Accepted: 04/07/2025] [Indexed: 04/29/2025] Open
Abstract
The rapidly emerging large-scale data in diverse biological research fields present valuable opportunities to explore the underlying mechanisms of tissue development and disease progression. However, few existing methods can simultaneously capture common and condition-specific association between different types of features across different biological conditions, such as cancer types or cell populations. Therefore, we developed the sparse tensor-based partial least squares (sTPLS) method, which integrates multiple pairs of datasets containing two types of features but derived from different biological conditions. We demonstrated the effectiveness and versatility of sTPLS through simulation study and three biological applications. By integrating the pairwise pharmacogenomic data, sTPLS identified 11 gene-drug comodules with high biological functional relevance specific for seven cancer types and two comodules that shared across multi-type cancers, such as breast, ovarian, and colorectal cancers. When applied to single-cell data, it uncovered nine gene-peak comodules representing transcriptional regulatory relationships specific for five cell types and three comodules shared across similar cell types, such as intermediate and naïve B cells. Furthermore, sTPLS can be directly applied to tensor-structured data, successfully revealing shared and distinct cell communication patterns mediated by the MK signaling pathway in coronavirus disease 2019 patients and healthy controls. These results highlight the effectiveness of sTPLS in identifying biologically meaningful relationships across diverse conditions, making it useful for multi-omics integrative analysis.
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Affiliation(s)
- Jinyu Chen
- School of Mathematics, Statistics and Mechanics, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Wenwen Min
- School of Information Science and Engineering, Yunnan University, East Outer Ring Road, Chenggong District, Kunming 650500, China
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Da Silva Filho J, Herder V, Gibbins MP, Dos Reis MF, Melo GC, Haley MJ, Judice CC, Val FFA, Borba M, Tavella TA, de Sousa Sampaio V, Attipa C, McMonagle F, Wright D, de Lacerda MVG, Costa FTM, Couper KN, Marcelo Monteiro W, de Lima Ferreira LC, Moxon CA, Palmarini M, Marti M. A spatially resolved single-cell lung atlas integrated with clinical and blood signatures distinguishes COVID-19 disease trajectories. Sci Transl Med 2024; 16:eadk9149. [PMID: 39259811 DOI: 10.1126/scitranslmed.adk9149] [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: 09/26/2023] [Revised: 02/15/2024] [Accepted: 08/05/2024] [Indexed: 09/13/2024]
Abstract
COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology during acute COVID-19 is necessary to understand its diverse pathogenesis and inform more effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood markers, and lung pathology in 142 Brazilian patients hospitalized with COVID-19. We identified core clinical and peripheral blood signatures differentiating disease progression between patients who recovered from severe disease compared with those who succumbed to the disease. Signatures were heterogeneous among fatal cases yet clustered into two patient groups: "early death" (<15 days until death) and "late death" (>15 days). Progression to early death was characterized systemically and in lung histopathological samples by rapid endothelial and myeloid activation and the presence of thrombi associated with SARS-CoV-2+ macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis, and SARS-CoV-2+ epithelial cells in postmortem lung tissue. In late death cases, cytotoxicity, interferon, and T helper 17 (TH17) signatures were only detectable in the peripheral blood after 2 weeks of hospitalization. Progression to recovery was associated with higher lymphocyte counts, TH2 responses, and anti-inflammatory-mediated responses. By integrating antemortem longitudinal blood signatures and spatial single-cell lung signatures from postmortem lung samples, we defined clinical parameters that could be used to help predict COVID-19 outcomes.
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Affiliation(s)
- João Da Silva Filho
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, UK
- Institute of Parasitology Zurich (IPZ), VetSuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Vanessa Herder
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Matthew P Gibbins
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, UK
- Institute of Parasitology Zurich (IPZ), VetSuisse Faculty, University of Zurich, Zurich, Switzerland
| | - Monique Freire Dos Reis
- Department of Education and Research, Oncology Control Centre of Amazonas State (FCECON), Manaus, Brazil
- Postgraduate Program in Tropical Medicine, University of Amazonas State, Manaus, Brazil
- Federal University of Amazonas, Manaus, Brazil
- Amazonas Oncology Control Center Foundation, Manaus, Brazil
| | | | - Michael J Haley
- Department of Immunology, Immunity to Infection and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Carla Cristina Judice
- Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas, Campinas, Brazil
| | - Fernando Fonseca Almeida Val
- Postgraduate Program in Tropical Medicine, University of Amazonas State, Manaus, Brazil
- Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Brazil
| | - Mayla Borba
- Postgraduate Program in Tropical Medicine, University of Amazonas State, Manaus, Brazil
- Delphina Rinaldi Abdel Aziz Emergency Hospital (HPSDRA), Manaus, Brazil
| | - Tatyana Almeida Tavella
- Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas, Campinas, Brazil
- INSERM U1016, CNRS UMR8104, University of Paris Cité, Institut Cochin, Paris, France
| | | | - Charalampos Attipa
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, UK
- Department of Tropical Disease Biology, Liverpool School of Tropical Medicine, Liverpool, UK
- Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Edinburgh, UK
| | - Fiona McMonagle
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, UK
- Glasgow Imaging Facility/School of Infection and Immunity, University of Glasgow, Glasgow, UK
| | - Derek Wright
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
| | - Marcus Vinicius Guimaraes de Lacerda
- Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Brazil
- Instituto Leônidas e Maria Deane, Fiocruz, Manaus, Brazil
- University of Texas Medical Branch, Galveston, TX, USA
| | | | - Kevin N Couper
- Department of Immunology, Immunity to Infection and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Wuelton Marcelo Monteiro
- Postgraduate Program in Tropical Medicine, University of Amazonas State, Manaus, Brazil
- Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Brazil
| | - Luiz Carlos de Lima Ferreira
- Postgraduate Program in Tropical Medicine, University of Amazonas State, Manaus, Brazil
- Tropical Medicine Foundation Dr. Heitor Vieira Dourado, Manaus, Brazil
| | - Christopher Alan Moxon
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, UK
- (C.A.M.)
| | - Massimo Palmarini
- MRC-University of Glasgow Centre for Virus Research, Glasgow, UK
- (M.P.)
| | - Matthias Marti
- Wellcome Centre for Integrative Parasitology, School of Infection and Immunity, University of Glasgow, Glasgow, UK
- Institute of Parasitology Zurich (IPZ), VetSuisse Faculty, University of Zurich, Zurich, Switzerland
- (M.M.)
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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Controlling the confounding effect of metabolic gene expression to identify actual metabolite targets in microsatellite instability cancers. Hum Genomics 2023; 17:18. [PMID: 36879264 PMCID: PMC9990231 DOI: 10.1186/s40246-023-00465-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 02/22/2023] [Indexed: 03/08/2023] Open
Abstract
BACKGROUND The metabolome is the best representation of cancer phenotypes. Gene expression can be considered a confounding covariate affecting metabolite levels. Data integration across metabolomics and genomics to establish the biological relevance of cancer metabolism is challenging. This study aimed to eliminate the confounding effect of metabolic gene expression to reflect actual metabolite levels in microsatellite instability (MSI) cancers. METHODS In this study, we propose a new strategy using covariate-adjusted tensor classification in high dimensions (CATCH) models to integrate metabolite and metabolic gene expression data to classify MSI and microsatellite stability (MSS) cancers. We used datasets from the Cancer Cell Line Encyclopedia (CCLE) phase II project and treated metabolomic data as tensor predictors and data on gene expression of metabolic enzymes as confounding covariates. RESULTS The CATCH model performed well, with high accuracy (0.82), sensitivity (0.66), specificity (0.88), precision (0.65), and F1 score (0.65). Seven metabolite features adjusted for metabolic gene expression, namely, 3-phosphoglycerate, 6-phosphogluconate, cholesterol ester, lysophosphatidylethanolamine (LPE), phosphatidylcholine, reduced glutathione, and sarcosine, were found in MSI cancers. Only one metabolite, Hippurate, was present in MSS cancers. The gene expression of phosphofructokinase 1 (PFKP), which is involved in the glycolytic pathway, was related to 3-phosphoglycerate. ALDH4A1 and GPT2 were associated with sarcosine. LPE was associated with the expression of CHPT1, which is involved in lipid metabolism. The glycolysis, nucleotide, glutamate, and lipid metabolic pathways were enriched in MSI cancers. CONCLUSIONS We propose an effective CATCH model for predicting MSI cancer status. By controlling the confounding effect of metabolic gene expression, we identified cancer metabolic biomarkers and therapeutic targets. In addition, we provided the possible biology and genetics of MSI cancer metabolism.
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Ahern DJ, Ai Z, Ainsworth M, Allan C, Allcock A, Angus B, Ansari MA, Arancibia-Cárcamo CV, Aschenbrenner D, Attar M, Baillie JK, Barnes E, Bashford-Rogers R, Bashyal A, Beer S, Berridge G, Beveridge A, Bibi S, Bicanic T, Blackwell L, Bowness P, Brent A, Brown A, Broxholme J, Buck D, Burnham KL, Byrne H, Camara S, Candido Ferreira I, Charles P, Chen W, Chen YL, Chong A, Clutterbuck EA, Coles M, Conlon CP, Cornall R, Cribbs AP, Curion F, Davenport EE, Davidson N, Davis S, Dendrou CA, Dequaire J, Dib L, Docker J, Dold C, Dong T, Downes D, Drakesmith H, Dunachie SJ, Duncan DA, Eijsbouts C, Esnouf R, Espinosa A, Etherington R, Fairfax B, Fairhead R, Fang H, Fassih S, Felle S, Fernandez Mendoza M, Ferreira R, Fischer R, Foord T, Forrow A, Frater J, Fries A, Gallardo Sanchez V, Garner LC, Geeves C, Georgiou D, Godfrey L, Golubchik T, Gomez Vazquez M, Green A, Harper H, Harrington HA, Heilig R, Hester S, Hill J, Hinds C, Hird C, Ho LP, Hoekzema R, Hollis B, Hughes J, Hutton P, Jackson-Wood MA, Jainarayanan A, James-Bott A, Jansen K, Jeffery K, Jones E, Jostins L, Kerr G, Kim D, Klenerman P, Knight JC, Kumar V, et alAhern DJ, Ai Z, Ainsworth M, Allan C, Allcock A, Angus B, Ansari MA, Arancibia-Cárcamo CV, Aschenbrenner D, Attar M, Baillie JK, Barnes E, Bashford-Rogers R, Bashyal A, Beer S, Berridge G, Beveridge A, Bibi S, Bicanic T, Blackwell L, Bowness P, Brent A, Brown A, Broxholme J, Buck D, Burnham KL, Byrne H, Camara S, Candido Ferreira I, Charles P, Chen W, Chen YL, Chong A, Clutterbuck EA, Coles M, Conlon CP, Cornall R, Cribbs AP, Curion F, Davenport EE, Davidson N, Davis S, Dendrou CA, Dequaire J, Dib L, Docker J, Dold C, Dong T, Downes D, Drakesmith H, Dunachie SJ, Duncan DA, Eijsbouts C, Esnouf R, Espinosa A, Etherington R, Fairfax B, Fairhead R, Fang H, Fassih S, Felle S, Fernandez Mendoza M, Ferreira R, Fischer R, Foord T, Forrow A, Frater J, Fries A, Gallardo Sanchez V, Garner LC, Geeves C, Georgiou D, Godfrey L, Golubchik T, Gomez Vazquez M, Green A, Harper H, Harrington HA, Heilig R, Hester S, Hill J, Hinds C, Hird C, Ho LP, Hoekzema R, Hollis B, Hughes J, Hutton P, Jackson-Wood MA, Jainarayanan A, James-Bott A, Jansen K, Jeffery K, Jones E, Jostins L, Kerr G, Kim D, Klenerman P, Knight JC, Kumar V, Kumar Sharma P, Kurupati P, Kwok A, Lee A, Linder A, Lockett T, Lonie L, Lopopolo M, Lukoseviciute M, Luo J, Marinou S, Marsden B, Martinez J, Matthews PC, Mazurczyk M, McGowan S, McKechnie S, Mead A, Mentzer AJ, Mi Y, Monaco C, Montadon R, Napolitani G, Nassiri I, Novak A, O'Brien DP, O'Connor D, O'Donnell D, Ogg G, Overend L, Park I, Pavord I, Peng Y, Penkava F, Pereira Pinho M, Perez E, Pollard AJ, Powrie F, Psaila B, Quan TP, Repapi E, Revale S, Silva-Reyes L, Richard JB, Rich-Griffin C, Ritter T, Rollier CS, Rowland M, Ruehle F, Salio M, Sansom SN, Sanches Peres R, Santos Delgado A, Sauka-Spengler T, Schwessinger R, Scozzafava G, Screaton G, Seigal A, Semple MG, Sergeant M, Simoglou Karali C, Sims D, Skelly D, Slawinski H, Sobrinodiaz A, Sousos N, Stafford L, Stockdale L, Strickland M, Sumray O, Sun B, Taylor C, Taylor S, Taylor A, Thongjuea S, Thraves H, Todd JA, Tomic A, Tong O, Trebes A, Trzupek D, Tucci FA, Turtle L, Udalova I, Uhlig H, van Grinsven E, Vendrell I, Verheul M, Voda A, Wang G, Wang L, Wang D, Watkinson P, Watson R, Weinberger M, Whalley J, Witty L, Wray K, Xue L, Yeung HY, Yin Z, Young RK, Youngs J, Zhang P, Zurke YX. A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. Cell 2022; 185:916-938.e58. [PMID: 35216673 PMCID: PMC8776501 DOI: 10.1016/j.cell.2022.01.012] [Show More Authors] [Citation(s) in RCA: 179] [Impact Index Per Article: 59.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/16/2021] [Accepted: 01/17/2022] [Indexed: 02/06/2023]
Abstract
Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19.
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