Peter Busse, Ramón Díaz
What are the television viewing and eating habits of children in Peru?
Global health promotion
singlecell rna sequencing scrnaseq technology provides means studying biology cellular perspective fundamental goal scrnaseq data analysis discriminate singlecell types using unsupervised clustering singlecell clustering algorithms taken account deep surface information despite recent slew suggestions consequently article constructs fusion learning framework based deep learning namely scgasi learning clustering similarity matrix scgasi integrates data affinity recovery deep feature embedding unified scheme based various top feature sets next scgasi learns lowdimensional latent representation underlying data using graph autoencoder mine hidden information residing data efficiently merge surface information raw area deeper potential information underlying area construct fusion learning model based selfexpression scgasi uses fusion learning model learn similarity matrix individual feature set well clustering similarity matrix feature sets lastly gene marker identification visualization clustering accomplished using clustering similarity matrix extensive verification actual data sets demonstrates scgasi outperforms many widely used clustering techniques terms clustering accuracy