Predicting deleterious missense genetic variants via integrative supervised nonnegative matrix tri-factorizationAmong an assortment of genetic variations, Missense are major ones which a small subset of them may
Integrative Non-negative Matrix and Tensor Decomposition INMTD (Integrative Non-negative Matrix and Tensor Decomposition) is a novel multi-view clustering method which integrates 2D and 3D datasets for joint clustering and removes confounding effects. It learns an embedding matrix for each data dimension...
In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower ...
LIGER applies an integrative nonnegative matrix factorization (iNMF) approach to project the data onto a lower-dimensional space and then builds a shared nearest neighbor graph for joint clustering. Signac implements canonical correlation analysis (CCA) for dimensionality reduction; Signac subsequently ...
CCA, canonical correlation analysis; MOFA, multi-omics factor analysis; NMF, non-negative matrix factorization. Full size image Statistical approaches for multimodal single-cell integration are likely to be inspired by bulk approaches (reviewed elsewhere76) that are used to perform joint dimensionality ...
Non-negative matrix factorization by maximizing correntropy for cancer clustering. BMC Bioinformatics. 2013;14(1):107. 30. Ievgen R, Younes B. Controlling orthogonality constraints for better NMF clustering. In: International Joint Conference on Neural Networks; 2014. p. 3894–900. 31. Ding C, ...
For miRNA sequencing data we used unsupervised non-negative matrix factorization (NMF) consensus clustering (v0.20.5) in R 3.1.2, with default settings (Gaujoux and Seoighe, 2010). The input was a reads-per-million (RPM) data matrix for the 303 (25% of 1212 miRBase v16) most-variant...
For integrative clustering analysis joint non-negative matrix factorization (jNMF) has been adopted to identify latent features by combining heterogeneous data simultaneously. However, jNMF problem is iterative in nature so the initialization of the problem extensively affects the convergence rate and the...
Clustering via unsupervised non-negative matrix factorization (NMF) was conducted on the expression profiles of metabolism-related genes utilizing the NMF package, which is based on the TCGA database. The relationship between all candidate genes and OS was assessed through the use of the “survival...
To explore the specific etiological factors that might contribute to the mutagenesis of ESCC, we adopted a non-negative matrix factorization (NMF) algorithm27to extract mutational signatures from the WES data of the Fudan cohort and other ESCC cohorts, such as Moody’s cohort22, TCGA cohort15, ...