Table 1.

List of computational frameworks for multi-omics cancer studies

Study	Findings	Dataset	Principles
iCluster (Curtis et al., 2012; Shen et al., 2009)	Novel subgroups from 2,000 breast tumors	mRNA expressiona CNVc	Joint latent variable model-based clustering method
iOmicsPASS (Koh et al., 2019)	Novel transcriptional regulatory network from TCGA/CPTAC breast cancer data	mRNA expressiona CNVd Protein expressione	Network construction using a modified nearest shrunken centroid algorithm
SALMON (Huang et al., 2019)	Improved survival analysis	Mutationh mRNA/miRNA expression CNVh	Deep learning based on co-expression modules
SNF (Wang et al., 2014)	Subtype classification of clinical relevance	mRNAa/miRNA expressionb DNA methylationg	Patient similarity networks using an iterative procedure based on message passing
NEMO (Rappoport and Shamir, 2019)	Novel subtypes from even partial AML datasets	mRNAa/miRNA expressionb DNA methylationg	Sample clustering from partial datasets using an adjusted Rand index
MONET (Rappoport et al., 2020)	Module detection of patient subtypes and improved survival analysis	mRNAa/miRNA expressionb DNA methylationg	Detect similar modules commonly present across multi-omics datasets
PARADIGM (Vaske et al., 2010)	Detection of pathways affected by cancer with fewer false positives	mRNA expressiona CNVc	Pathway recognition algorithm applied to multi-omics datasets
LRAcluster (Wu et al., 2015)	Subtype detection in both pan-cancer analysis and single cancer types	Mutationi mRNA expressiona CNVd DNA methylationg	Performance of low-rank approximation from probabilistic models
BCC (Lock and Dunson, 2013)	Detection of patient subtypes in response to survival rates and driver mutation signatures	mRNAa/miRNA expressionb DNA methylationg Protein expressionf	Bayesian framework for estimation of an integrative clustering model

^aGene expression data with normalization (e.g., quantile normalization, fragment per kilobase of transcript per million mapped reads [FPKM]).

^bQuantification of miRNA expression.

^cCircular binary segmentation-based copy number segmented means.

^dAffymetrix 6.0 SNP arrays.

^eProtein quantification by iTRAQ (isobaric Tags for Relative and Absolute Quantification) protein quantification.

^fReverse phase protein array (RPPA).

^gIllumina Human Methylation arrays.

^hIn the SALMON method, the copy number burden (CNB) is calculated using the total gene length (Kb) from SNP 6 data, and the tumor mutation burden (TMB) is calculated using the total number of mutated genes reported in Mutation Annotation Format (MAF) files.

ⁱThe LRAcluster method uses somatic mutation data converted into a binary form.