TABLE 2.

Four scenarios with recommended methods

Scenarios	Required characteristics for method	Recommended methods
I. (Feature selection): The need to identify clinically relevant disease subtypes and driving molecular signatures which can be targeted for treatment	Performing both sample clustering and feature selection	iCluster; iClusterPlus; iClusterBayes; intNMF; IS-K means; CIMLR; PSDF
II. (Mixed-type data): Large scale genomic data of mixed-type in large consortia	Integrating mixed type of data	iClusterPlus; iClusterBayes; moCluster; LRAcluster; MDI; SNF; CIMLR; rMKL-LPP; PINS; PINSPlus
III. (Computational efficiency): Concern on the computational resources and consumption of time	Computationally efficient	Spectrum; SNF; ab-SNF; NEMO; CIMLR; rMKL-LPP
IV. (Knowledge integration): Leveraging the prior knowledge	Incorporating prior information	IS-K means; PARADIGM