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CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data
Author(s) -
Ziyang Wei,
Shuqin Zhang
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab286
Subject(s) - annotation , computer science , cluster analysis , graph , supervised learning , artificial intelligence , data mining , machine learning , semi supervised learning , laplacian matrix , pattern recognition (psychology) , theoretical computer science , artificial neural network
Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation.

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