Article

Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

2016-03-09

Abstract excerpt

Single-cell RNA-Sequencing (scRNA-Seq) is a cutting edge technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we have investigated the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq data set...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 55c1fe3e-8e54-563c-bd2e-be32593980e6
DOI: 10.7287/peerj.preprints.1839v2

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorizationDOI 10.7287/peerj.preprints.1839v2Read

KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq dataSemantic proximity Read

Learning common and specific patterns from data of multiple interrelated biological scenarios with matrix factorizationSemantic proximity Read

Detecting cell type from single cell RNA sequencing based on deep bi-stochastic graph regularized matrix factorizationSemantic proximity Read

Resolving malignant cell heterogeneity from bulk tumor RNA-seq data with CDStateSemantic proximity Read

Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression dataSemantic proximity Read

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysisSemantic proximity Read

scAURA: Alignment- and Uniformity-based Graph Debiased Contrastive Representation Architecture for Self-Supervised Clustering of Single-Cell TranscriptomicsSemantic proximity Read

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profilesSemantic proximity Read

NMFNA: a non-negative matrix factorization network analysis method for identifying communities and characteristic genes from pancreatic cancer dataSemantic proximity Read

Single-cell histone chaperones patterns guide intercellular communication of tumor microenvironment that contribute to breast cancer metastasesSemantic proximity Read

Extraction of biological signals by factorization enables the reliable analysis of single-cell transcriptomicsSemantic proximity Read

The Tumor Suppressor Role of Zinc Finger Protein 671 (ZNF671) in Multiple Tumors Based on Cancer Single-Cell SequencingSemantic proximity Read