A whitening approach to probabilistic canonical correlation analysis for omics data integration

Jendoubi, Takoua; Strimmer, Korbinian

doi:10.1186/s12859-018-2572-9

Cited by 40 publications

(33 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, Bylesjö et al [ 1 ] used the genomic variation present in the genomic dataset to harness the inter-individual heterogeneity resulting from baseline fluctuation and differentiate it from treatment induced variation. However, integrative analysis sometime needs robust models to account for individual variations [ 53 , 59 ].…”

Section: Challenges In Metabolomics and Multi-omics Data Integratimentioning

confidence: 99%

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Jendoubi

2021

Metabolites

Self Cite

View full text Add to dashboard Cite

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria — hypothesis, data types, strategies, study design and study focus — to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

show abstract

Section: Challenges In Metabolomics and Multi-omics Data Integratimentioning

confidence: 99%

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Jendoubi

2021

Metabolites

Self Cite

View full text Add to dashboard Cite

show abstract

“…Canonical Correlation Analysis (CCA) 70 was applied in order to investigate patterns of association between genes related to CMSP and NMI genes, considering the observations from the SC2 group alone. We retained the first two canonical variates (CVs) for subsequent interpretations.…”

Section: Canonical Correlation Analysismentioning

confidence: 99%

“…The sample median and sample interquartile range were calculated using R software version 4.0.2 (https://www.r-project.org/index.html). The NP-MANOVA, CCA, MVR, and Ridge Regression analysis were all performed on the R software version 4.0.2.Specifically, for NP-MANOVA, it was used the npmv package68 for CCA the whitening70 ,DFA, and CANCOR packages, for Ridge Regression the glmnet package and for PCA the psych package. MVR was implemented by the authors following the results of García et al(2003)…”

mentioning

confidence: 99%

Specific immune-regulatory transcriptional signatures reveal sex and age differences in SARS-CoV-2 infected patients

Freire¹,

Marques²,

Baiocchi³

et al. 2020

Preprint

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) fatality rate varies in different patient groups. However, the underlying mechanisms that explain this variation are poorly understood. Here, we reanalyzed and integrated public RNAseq datasets of nasopharyngeal swabs and peripheral blood leukocytes from patients with SARS-CoV-2, comparing transcription patterns according to sex, age, and viral load. We found that female and young patients infected by SARS-CoV-2 exhibited a similar transcriptomic pattern with a larger number of total (up- and downregulated) differentially expressed genes (DEGs) compared to males and elderly patients. The transcriptional analysis showed a sex-specific profile with a higher transcriptional modulation of immune response-associated genes in female and young subjects against SARS-CoV-2. The functional clustering was characterized by a highly correlated interferome network of cytokine/chemokine- and neutrophil-associated genes that were enriched both in nasopharyngeal cells and peripheral blood of COVID-19 patients. Females exhibited reduced transcriptional levels of key pro-inflammatory/neutrophil-related genes such as CXCL8 receptors (CXCR1/CXCR2), IL-1β, S100A9, ITGAM, and DBNL compared to males, which correlate with a protective gene expression profile against inflammatory damage. Our data indicate specific immune-regulatory pathways associated with sex and age of patients infected with SARS-CoV-2. These results point out therapeutic targets to reduce morbidity and mortality of COVID-19.

show abstract

“…Jendoubi et al proposed a CCA probabilistic model in the form of a two-layer latent variable model and used for integrated analysis of gene expression data, lipid concentration, and methylation level omics datasets. It provided a new strategy to unify the spheroidization process, multiple regression, and the corresponding probability model [ 31 ]. In addition, deep learning has been widely applied in many areas for big data analysis.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis of a Weak Correlation between Complicated Data on the Basis of Principal Component Analysis

Pang

Zhang

Wen³

et al. 2021

Journal of Analytical Methods in Chemistry

View full text Add to dashboard Cite

The mining of weak correlation information between two data matrices with high complexity is a very challenging task. A new method named principal component analysis-based multiconfidence ellipse analysis (PCA/MCEA) was proposed in this study, which first applied a confidence ellipse to describe the difference and correlation of such information among different categories of objects/samples on the basis of PCA operation of a single targeted data. This helps to find the number of objects contained in the overlapping and nonoverlapping areas of ellipses obtained from PCA runs. Then, a quantitative evaluation index of correlation between data matrices was defined by comparing the PCA results of more than one data matrix. The similarity and difference between data matrices was further quantified through comprehensively analyzing the outcomes. Complicated data of tobacco agriculture were used as an example to illustrate the strategy of the proposed method, which includes rich features of climate, altitude, and chemical compositions of tobacco leaves. The number of objects of these data reached 171,516 with 14, 4, and 5 descriptors of climate, altitude, and chemicals, respectively. On the basis of the new method, the complex but weak relationship between these independent and dependent variables were interestingly studied. Three widely used but conventional methods were applied for comparison in this work. The results showed the power of the new method to discover the weak correlation between complicated data.

show abstract

A whitening approach to probabilistic canonical correlation analysis for omics data integration

Cited by 40 publications

References 28 publications

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Specific immune-regulatory transcriptional signatures reveal sex and age differences in SARS-CoV-2 infected patients

Quantitative Analysis of a Weak Correlation between Complicated Data on the Basis of Principal Component Analysis

Contact Info

Product

Resources

About