[HTML][HTML] clustifyr: an R package for automated single-cell RNA sequencing cluster classification
Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-
consuming and error-prone process. Current packages for identity assignment use limited …
consuming and error-prone process. Current packages for identity assignment use limited …
SCSA: a cell type annotation tool for single-cell RNA-seq data
Y Cao, X Wang, G Peng - Frontiers in genetics, 2020 - frontiersin.org
Currently most methods take manual strategies to annotate cell types after clustering the
single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and …
single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and …
Evaluation of cell type annotation R packages on single-cell RNA-seq data
Annotating cell types is a critical step in single-cell RNA sequencing (scRNA-seq) data
analysis. Some supervised or semi-supervised classification methods have recently …
analysis. Some supervised or semi-supervised classification methods have recently …
scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data
Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the
classification of thousands of cells through transcriptome profiling, wherein accurate cell …
classification of thousands of cells through transcriptome profiling, wherein accurate cell …
[HTML][HTML] Automated methods for cell type annotation on scRNA-seq data
G Pasquini, JER Arias, P Schäfer… - Computational and …, 2021 - Elsevier
The advent of single-cell sequencing started a new era of transcriptomic and genomic
research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type …
research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type …
Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction
Background Cell type identification is one of the most important questions in single-cell RNA
sequencing (scRNA-seq) data analysis. With the accumulation of public scRNA-seq data …
sequencing (scRNA-seq) data analysis. With the accumulation of public scRNA-seq data …
CIPR: a web-based R/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments
Background Single cell RNA sequencing (scRNAseq) has provided invaluable insights into
cellular heterogeneity and functional states in health and disease. During the analysis of …
cellular heterogeneity and functional states in health and disease. During the analysis of …
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis
Clustering and cell type classification are important steps in single-cell RNA-seq (scRNA-
seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell …
seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell …
A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq
The cell type identification is among the most important tasks in single-cell RNA-sequencing
(scRNA-seq) analysis. Many in silico methods have been developed and can be roughly …
(scRNA-seq) analysis. Many in silico methods have been developed and can be roughly …
scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-seq data with batch effect
The power of single-cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell
type-dependent phenotypes, which rests on the accuracy of cell type identification. However …
type-dependent phenotypes, which rests on the accuracy of cell type identification. However …