Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations

Dutta, Diptavo; Sen, Ananda; Satagopan, Jaya M.

doi:10.1371/journal.pone.0276886

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…We utilized a sparse canonical correlation analysis (sCCA) to evaluate the relationship between high dimensional gut microbiome data and the comprehensive adaptive immune T cell phenotype by preserving the main facets that explain the correlation between two feature sets. sCCA is commonly used in genomics [20,21] or neuroscience [21][22][23] research areas to analyze high-dimensional data. This method allowed us to evaluate a selected set of T cell subsets and bacterial genera that had the highest correlation in the canonical component.…”

Section: Discussionmentioning

confidence: 99%

Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study

Vivek,

Shen,

Guan

et al. 2024

IJMS

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Though the microbiome’s impact on immune system homeostasis is well documented, the effect of circulating T cells on the gut microbiome remains unexamined. We analyzed data from 50 healthy volunteers in a pilot trial of aspirin, using immunophenotyping and 16S rRNA sequencing to evaluate the effect of baseline T cells on microbiome changes over 6 weeks. We employed an unsupervised sparse canonical correlation analysis (sCCA) and used multivariable linear regression models to evaluate the association between selected T cell subsets and selected bacterial genera after adjusting for covariates. In the cross-sectional analysis, percentages of naïve CD4+ T cells were positively associated with a relative abundance of Intestinimonas, and the percentage of activated CD8+ T cells was inversely associated with Cellulosibacter. In the longitudinal analysis, the baseline percentages of naïve CD4+ T cells and activated CD4+ T cells were inversely associated with a 6-week change in the relative abundance of Clostridium_XlVb and Anaerovorax, respectively. The baseline percentage of terminal effector CD4+ T cells was positively associated with the change in Flavonifractor. Notably, the microbiome taxa associated with T cell subsets exclusively belonged to the Bacillota phylum. These findings can guide future experimental studies focusing on the role of T cells in impacting gut microbiome homeostasis.

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Section: Discussionmentioning

confidence: 99%

Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study

Vivek,

Shen,

Guan

et al. 2024

IJMS

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Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis—An application in renal clear cell carcinoma

Dutta,

Sen,

Satagopan

2024

Genetic Epidemiology

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Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing “gene component scores” and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA‐KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of ASAH1 gene trans‐regulated by methylation of several genes including SIX5 and by CNAs in the 10q25 region including TCF7L2. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia‐regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.

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De-biased sparse canonical correlation for identifying cancer-related trans-regulated genes

Huey,

Dutta,

Laha

2024

Preprint

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In cancer multi-omic studies, identifying effect of somatic copy number aberrations (CNA) on physically distal gene expressions (trans associations) can potentially uncover genes critical for cancer pathogenesis. Sparse canonical correlation analysis (SCCA) has emerged as a promising method for identifying associations in high-dimensional settings, owing to its ability to aggregate weaker associations and its improved interpretability. Traditional SCCA lacks hypothesis testing capabilities, which are critical for controlling false discoveries. This limitation has recently been addressed through a bias correction technique that enables calibrated hypothesis testing. In this article, we leverage the theoretical advancements in de-biased SCCA to present a computationally efficient pipeline for multi-omics analysis. This pipeline identifies and tests associations between multi-omics data modalities in biomedical settings, such as the trans-effects of CNA on gene expression. We propose a detailed algorithm to choose the tuning parameters of de-biased SCCA. Applying this pipeline to data on estrogen receptor (ER)-associated CNAs and 10,756 gene expressions from 1,904 breast cancer patients in the METABRIC study, we identified 456 CNAs trans-associated with 256 genes. Among these, 5 genes were identified only through de-biased SCCA and not by the standard pairwise regression approach. Downstream analysis with the 256 genes revealed that these genes were overrepresented in pathways relevant to breast cancer.

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Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations

Cited by 4 publications

References 63 publications

Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study

Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study

Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis—An application in renal clear cell carcinoma

De-biased sparse canonical correlation for identifying cancer-related trans-regulated genes

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