Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

By Ronglai Shen, Adam B. Olshen, Marc Ladanyi. Bioinform., 2009

815 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/101939d1b491ef489247b5679ff100682fa23d53

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In Molecular Biology

Citations in the corpus

JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES (2011, methodology): “This direct approach using the aggregated data matrix is also utilized by the iCluster method [Shen et al., 2009].”
Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma (2017, positive): “…molecular platforms of HCC, we utilized iCluster, which formulates the problem of subgroup discovery as a joint multivariate regression of multiple data types with reference to a set of common latent variables that represent the underlying tumor subtypes (Mo et al., 2013; Shen et al., 2009).”
Similarity network fusion for aggregating data types on a genomic scale (2014, methodology): “We compared SNF to iCluster 7 and the concatenation of the three types of data ( Supplementary Note 3 ).”
Integration strategies of multi-omics data for machine learning analysis (2021, methodology): “MOFA + found predictive biomarkers from DNA methylation and gene expression data in cardiovascular disease [144]. iCluster [145] Gaussian latent variable model Generalized linear regression Bayesian integrative clustering Disease subtyping, biomarker discovery iCluster was used to identify subtypes…”
Pattern discovery and cancer gene identification in integrated cancer genomic data (2013, methodology): “In a previous publication (8), we proposed an integrative clustering framework called iCluster.”
Discovery of multi-dimensional modules by integrative analysis of cancer genomic data (2012, background): “(24) have proposed a joint clustering model for multiple genomic datasets, but it was designed for sample clustering and subtype discovery and cannot identify modules comprising of correlated variables.”
More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, methodology): “…matrices, but also proper normalization step for these input data sets as they have quite diﬀerent distributions and variability. iCluster Like NMF, iCluster (Shen et al., 2009, 2012) assumes a regularized joint latent variable, which is similar to W in NMF but without non-negative constraints.”
Methods for the integration of multi-omics data: mathematical aspects (2016, methodology): “For example, Conexic [11] is tailored for DNA copy number variations (CNV) and gene expression data, while iCluster [12] can be in principle used for the analysis of any combination of omics encoded as quantitative values on the same set of samples (Table 1).”
Multi-omics Data Integration, Interpretation, and Its Application (2020, methodology): “37 This unsupervised method uses a joint latent variable model for integrative clustering and incorporates flexible modeling of the associations between different data types and the variance-covariance structure within data types in a single framework while simultaneously reducing the…”
Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach (2015, methodology): “A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner.”