Carl F Weems, Bethany H McCurdy, Mikaela D Scozzafava
Toward a Developmental Model of Continuity and Change in PTSD Symptoms following Exposure to Traumatic and Adverse Experiences
Journal of child & adolescent trauma
technology analyzing singlecell multiomics data advanced rapidly provided comprehensive accurate cellular information exploring cell heterogeneity genomics transcriptomics epigenomics metabolomics proteomics data however highdimensional sparse characteristics singlecell multiomics data well limitations various analysis algorithms clustering performance generally poor matrix factorization unsupervised dimensionality reductionbased method cluster individuals discover related omics variables different blocks present novel algorithm performs joint dimensionality reduction learning cell clustering analysis singlecell multiomics data using nonnegative matrix factorization named scmnmf formulate objective function joint learning constrained optimization problem derive corresponding iterative formulas alternating iterative algorithms major advantage scmnmf algorithm remains capability explore hidden related features among omics data additionally feature selection dimensionality reduction cell clustering mutually influence iteratively leading effective discovery cell types validated performance scmnmf algorithm using two simulated five real datasets results show scmnmf outperformed seven stateoftheart algorithms various measurements scmnmf code found httpsgithubcomyushanqiuscmnmf