The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method i
Non-negative matrix factorization (NMF) was employed to assess the frequencies of mutated trinucleotide sequence motifs13,14. We identified five mutational signatures by Sigminer. These 5 signatures corresponded to the known COSMIC (Catalog of Somatic Mutations in Cancer) signatures: SBS30 (Defective D...
To evaluate whether ccRCC tumors could be partitioned into clusters with distinct metabolic phenotypes, we performed unsupervised non-negative matrix factorization (NMF)consensus clustering(Lee and Seung, 1999). This approach yielded four distinct clusters (Figure 4A). Mann-Whitney U tests were then u...
To further delineate the tumors based on the interactions among the intact glycopeptides and phosphopeptides, a multi-omics non-negative matrix factorization (NMF)-based clustering was conducted. Three cross-correlation clusters (CC 1–3) were derived and overlaid with the glycoproteomic subtypes (Fi...
NMFThe significant advantage of sparse methods is to reduce the complicacy of genes expression data, which makes them easier to understand and interpret. In this paper, we propose a novel Class-information-based Sparse Non-negative Matrix Factorization (CISNMF) method which introduces the class ...
Breast cancer clusters associated with CLMGs were identified through consistent clustering analysis of mRNA expression levels using the nonnegative matrix factorization (NMF) method, implemented with the NMF package (version 0.28) [24]. 2.3 Development of a risk model using CLMGs...
Non-negative Matrix Factorization; AOD: average optical density; IOD: integrated optical density; HR: hazard ratio; KM: Kaplan-Meier; GSEA: Gene Set Enrichment Analysis; L-dCAF: low-dCAF-infiltrating subtype; H-dCAF: high-dCAF-infiltrating subtype; DEGs: differentially expressed genes; IC50: half...
Theconsensus non-negative matrix factorization (cNMF)does exactly that: it infers the identity and activity of cellular signaling programs and their relative contributions in each cell. The authors used it to identify multiple gene expression programs exhibited by malignant cells and fibrobla...
Mutation signature discovery was performed using Bayesian non-negative matrix factorization algorithm for mutation signature analysis as described inSupplementary Information S3.2. Low-pass whole-genome sequencing for rearrangement identification Genomic DNA (500–700 ng per sample) was sheared into 250-bp...
(Supplementary Fig.5). Using non-negative matrix factorization, as described previously (see Methods), we identified six prominent mutational signatures (Supplementary Figs.5and6a, b). The strongest correlates included age (C > T mutations at NpCpG sites), an APOBEC signature (dominated by ...