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 …
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 …
SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles
P Xie, M Gao, C Wang, J Zhang, P Noel… - Nucleic acids …, 2019 - academic.oup.com
Abstract Characterization of individual cell types is fundamental to the study of multicellular
samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling …
samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling …
A web server for comparative analysis of single-cell RNA-seq data
Abstract Single cell RNA-Seq (scRNA-seq) studies profile thousands of cells in
heterogeneous environments. Current methods for characterizing cells perform …
heterogeneous environments. Current methods for characterizing cells perform …
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
Background Single-cell RNA-sequencing (scRNA-seq) is a transformative technology,
allowing global transcriptomes of individual cells to be profiled with high accuracy. An …
allowing global transcriptomes of individual cells to be profiled with high accuracy. An …
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 …
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 …
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 …
scClassify: sample size estimation and multiscale classification of cells using single and multiple reference
Automated cell type identification is a key computational challenge in single‐cell RNA‐
sequencing (sc RNA‐seq) data. To capitalise on the large collection of well‐annotated sc …
sequencing (sc RNA‐seq) data. To capitalise on the large collection of well‐annotated sc …
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through
unsupervised clustering, but the ever increasing number of cells and batch effect impose …
unsupervised clustering, but the ever increasing number of cells and batch effect impose …
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