Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets

Pomyen, Yotsawat; Segura, Marcelo; Ebbels, Timothy M. D.; Keun, Hector C.

doi:10.1093/bioinformatics/btu589

Cited by 13 publications

(7 citation statements)

References 17 publications

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“…Gene sets over-representation analysis (GSORA) based algorithm was implemented to assess whether a particular gene set is over-represented base on the hypergeometric test [ 25 ]. Terms were sorted by Z-score.…”

Section: Methodsmentioning

confidence: 99%

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes

et al. 2022

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Background Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis. Methods The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded. Results We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1–C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment. Conclusions This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management.

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Section: Methodsmentioning

confidence: 99%

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes

et al. 2022

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“…In addition, gene sets over-representation analysis (GSORA) was performed to evaluate the fraction of genes in tested gene clusters ( Pomyen et al, 2015 ). In this study, 54 septic shock-related gene sets downloaded from MSigDB were divided in nine subclusters on the basis of function features, which included C1–C8 and the hallmark pathway cluster (H) ( Liberzon et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of Key Immune-Related Genes in the Progression of Septic Shock

et al. 2021

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Objective: Septic shock is the severe complication of sepsis, with a high mortality. The inflammatory response regulates the immune status and mediates the progression of septic shock. In this study, we aim to identify the key immune-related genes (IRGs) of septic shock and explore their potential mechanism.Methods: Gene expression profiles of septic shock blood samples and normal whole blood samples were retrieved from the Gene Expression Omnibus (GEO) and Genotype-Tissue Expression Portal (GTEx). The differential expression genes (DEGs) and septic shock-specific immune-related genes (SSSIRGs) were evaluated and identified, along with the immune components by “cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT, version x)” algorithm. Additionally, in order to explore the key regulatory network, the relationship among SSSIRGs, upstream transcription factors (TFs), and downstream signaling pathways were also identified by Gene Set Variation Analysis (GSVA) and co-expression analysis. Moreover, the Connectivity Map (CMap) analysis was applied to find bioactive small molecules against the members of regulation network while Chromatin Immunoprecipitation sequencing (ChIP-seq) and Assay for Targeting Accessible-Chromatin with high-throughput sequencing (ATAC-seq) data were used to validate the regulation mechanism of the network.Results: A total of 14,843 DEGs were found between 63 septic shock blood samples and 337 normal whole blood samples. Then, we identified septic shock-specific 839 IRGs as the intersection of DEGs and IRGs. Moreover, we uncovered the regulatory networks based on co-expression analysis and found 28 co-expression interaction pairs. In the regulation network, protein phosphatase 3, catalytic subunit, alpha isozyme (PPP3CA) may regulate late estrogen response, glycolysis and TNFα signaling via NFκB and HLA; Kirsten rat sarcoma viral oncogene homolog (KRAS) may be related to late estrogen response and HLA; and Toll-like receptor 8 (TLR8) may be associated with TNFα signaling via NFκB. And the regulation mechanisms between TFs and IRGs (TLR8, PPP3CA, and KRAS) were validated by ChIP-seq and ATAC-seq.Conclusion: Our data identify three SSSIRGs (TLR8, PPP3CA, and KRAS) as candidate therapeutic targets for septic shock and provide constructed regulatory networks in septic shock to explore its potential mechanism.

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“…Nevertheless, statistically significant correlations reflect the probability of such a correlation occurring rather than its strength. Correlation coefficient strengths can be interpreted differently across scientific fields, and authors should avoid overinterpreting associations [ 51 , 66 , 67 ]. Based on prior work utilizing spearman rank correlation in the context of medicine and big data analysis, we have selected a correlation coefficient of 0.3 as the threshold between high and low correlation [ 51 ], or weak and moderate correlation [ 52 ].…”

Section: Limitationsmentioning

confidence: 99%

A fairness assessment of mobility-based COVID-19 case prediction models

Erfani,

Frias-Martinez

2023

PLoS ONE

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In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

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Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets

Abstract: The R code of the method is available at https://github.com/ORCABioinfo/ORCAcode.

Cited by 13 publications

References 17 publications

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes

Identification of Key Immune-Related Genes in the Progression of Septic Shock

A fairness assessment of mobility-based COVID-19 case prediction models

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