Cargando…

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

BACKGROUND: Canonical correlation analysis (CCA) is a classic statistical tool for investigating complex multivariate data. Correspondingly, it has found many diverse applications, ranging from molecular biology and medicine to social science and finance. Intriguingly, despite the importance and per...

Descripción completa

Detalles Bibliográficos
Autores principales:	Jendoubi, Takoua, Strimmer, Korbinian
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2019
Materias:	Methodology Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6327589/ https://www.ncbi.nlm.nih.gov/pubmed/30626338 http://dx.doi.org/10.1186/s12859-018-2572-9

Ejemplares similares

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer
por: Jendoubi, Takoua
Publicado: (2021)

Integrative analysis of time course metabolic data and biomarker discovery
por: Jendoubi, Takoua, et al.
Publicado: (2020)

A unified approach to false discovery rate estimation
por: Strimmer, Korbinian
Publicado: (2008)

From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
por: Opgen-Rhein, Rainer, et al.
Publicado: (2007)

A simple data-adaptive probabilistic variant calling model
por: Hoffmann, Steve, et al.
Publicado: (2015)

Group sparse canonical correlation analysis for genomic data integration
por: Lin, Dongdong, et al.
Publicado: (2013)

A general modular framework for gene set enrichment analysis
por: Ackermann, Marit, et al.
Publicado: (2009)

Inference of demographic history from genealogical trees using reversible jump Markov chain Monte Carlo
por: Opgen-Rhein, Rainer, et al.
Publicado: (2005)

A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies
por: Zuber, Verena, et al.
Publicado: (2012)

Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis
por: Ye, Wenbin, et al.
Publicado: (2019)

Robust sparse canonical correlation analysis
por: Wilms, Ines, et al.
Publicado: (2016)

Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach
por: de Tayrac, Marie, et al.
Publicado: (2009)

Meta-analytic support vector machine for integrating multiple omics data
por: Kim, SungHwan, et al.
Publicado: (2017)

Probabilistic principal component analysis for metabolomic data
por: Nyamundanda, Gift, et al.
Publicado: (2010)

A multivariate approach to the integration of multi-omics datasets
por: Meng, Chen, et al.
Publicado: (2014)

Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
por: Yoshida, Kosuke, et al.
Publicado: (2017)

An integrative U method for joint analysis of multi-level omic data
por: Geng, Pei, et al.
Publicado: (2019)

Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach
por: Boulesteix, Anne-Laure, et al.
Publicado: (2005)

Sparse reduced-rank regression for integrating omics data
por: Hilafu, Haileab, et al.
Publicado: (2020)

Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data
por: Min, Eun Jeong, et al.
Publicado: (2020)

Modeling gene expression measurement error: a quasi-likelihood approach
por: Strimmer, Korbinian
Publicado: (2003)

A data management infrastructure for the integration of imaging and omics data in life sciences
por: Kuhn Cuellar, Luis, et al.
Publicado: (2022)

A flexible representation of omic knowledge for thorough analysis of microarray data
por: Hasegawa, Yoshikazu, et al.
Publicado: (2006)

Microbial risk score for capturing microbial characteristics, integrating multi-omics data, and predicting disease risk
por: Wang, Chan, et al.
Publicado: (2022)

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge
por: Cho, Young-Rae, et al.
Publicado: (2008)

A probabilistic algorithm to process geolocation data
por: Merkel, Benjamin, et al.
Publicado: (2016)

Supervised Regularized Canonical Correlation Analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery
por: Golugula, Abhishek, et al.
Publicado: (2011)

Probabilistic base calling of Solexa sequencing data
por: Rougemont, Jacques, et al.
Publicado: (2008)

Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study
por: Rodosthenous, Theodoulos, et al.
Publicado: (2020)

Visualising associations between paired ‘omics’ data sets
por: González, Ignacio, et al.
Publicado: (2012)

Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge
por: Can, Handan, et al.
Publicado: (2023)

A probabilistic multi-omics data matching method for detecting sample errors in integrative analysis
por: Lee, Eunjee, et al.
Publicado: (2019)

Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks
por: Waaijenborg, Sandra, et al.
Publicado: (2009)

Increasing the information provided by probabilistic sensitivity analysis: The relative density plot
por: Geenen, Joost W., et al.
Publicado: (2020)

Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework
por: Voillet, Valentin, et al.
Publicado: (2016)

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis
por: Jiang, Min-Zhi, et al.
Publicado: (2023)

Identification of multimodal brain imaging association via a parameter decomposition based sparse multi-view canonical correlation analysis method
por: Zhang, Jin, et al.
Publicado: (2022)

Comparison of cancer subtype identification methods combined with feature selection methods in omics data analysis
por: Park, JiYoon, et al.
Publicado: (2023)

Multilevel omic data integration in cancer cell lines: advanced annotation and emergent properties
por: Liu, Yuanhua, et al.
Publicado: (2013)

Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis
por: Qi, Lin, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_tu2dlqbvkv6f2netp532vsjb4v