Article

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

2021-01-14

Abstract excerpt

Transcriptome profiling and differential gene expression constitute a ubiquitous tool in biomedical research and clinical application. Linear dimensionality reduction methods especially principal component analysis (PCA) are widely used in detecting sample-to-sample heterogeneity in bulk transcriptomic datasets so that appropriate analytic methods can be used to correct batch effects, remove outliers and distingui...

Topics

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

Identifiers and source

Literature Corpus work: 837a48bd-e2fa-59ab-bb26-6658aa50eb50
DOI: 10.1101/2021.01.12.426467

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic dataDOI 10.1101/2021.01.12.426467Read

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel PitfallsSemantic proximity Read

A Fully Automated, Data-Driven Approach for Dimensionality Reduction and Clustering in Single-Cell RNA-seq AnalysisSemantic proximity Read

Data-driven characterization of molecular phenotypes across heterogeneous sample collectionsSemantic proximity Read

InGene: Finding influential genes from embeddings of nonlinear dimension reduction techniquesSemantic proximity Read

Expression Atlas update: insights from sequencing data at both bulk and single cell level.Semantic proximity Read

Single-Cell Manifold Preserving Feature Selection (SCMER)Semantic proximity Read

A brand-new clustering method and analysis system designed for revealing the truth of the high-dimension large data deciphered the complex composition structure of human brain endothelial cells from single-cell RNA sequence dataSemantic proximity Read

Dimensionality reduction by UMAP to visualize physical and genetic interactions.Semantic proximity Read

Single-Cell Manifold Preserving Feature Selection (SCMER)Semantic proximity Read

Intrinsic-Dimension analysis for guiding dimensionality reduction and data-fusion in multi-omics data processingSemantic proximity Read

Heterogeneity-Preserving Discriminative Feature Selection for Disease-Specific Subtype DiscoverySemantic proximity Read

Diagnostic and Prognostic Implications of Ribosomal Protein Transcript Coordination in Human CancersSemantic proximity Read