Article

SCINA: Semi-Supervised Analysis of Single Cells <i>in silico</i>

2019-02-25

Abstract excerpt

<h4>ABSTRACT</h4> Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore the prior knowledge of transcriptomes and of the probable structures of the data. Moreover, cell identification heavily relies on subjective and inaccurate huma...

Topics

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

Identifiers and source

Literature Corpus work: 681c1bc8-a16a-540f-a61c-4f1b9b182042
DOI: 10.1101/559872

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

SCINA: Semi-Supervised Analysis of Single Cells <i>in silico</i>DOI 10.1101/559872Read

scQuery: a web server for comparative analysis of single-cell RNA-seq dataSemantic proximity Read

<i>Besca</i> , a single-cell transcriptomics analysis toolkit to accelerate translational researchSemantic proximity Read

CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq dataSemantic proximity Read

Single-Cell Signature Explorer for comprehensive visualization of single cell signatures across scRNA-seq data setsSemantic proximity Read

Unsupervised multi-scale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypesSemantic proximity Read

SCENIC: Single-cell regulatory network inference and clusteringSemantic proximity Read

<i>Cytocipher</i> determines significantly different populations of cells in single cell RNA-seq dataSemantic proximity Read

A Universal Algorithm to Detect Rare or Novel Cell Types in High-Throughput Single-Cell Gene Expression DataSemantic proximity Read

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self-Organizing MapsSemantic proximity Read

Semi-automated annotation refinement accelerates cell type identification in brain spatial and single-cell studiesSemantic proximity Read

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic dataSemantic proximity Read

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomesSemantic proximity Read