Classification of low quality cells from single-cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical
research. One of the key challenges is to ensure that only single, live cells are included in …
research. One of the key challenges is to ensure that only single, live cells are included in …
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 …
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
Single-cell RNA sequencing has enabled the characterization of highly specific cell types in
many tissues, as well as both primary and stem cell-derived cell lines. An important facet of …
many tissues, as well as both primary and stem cell-derived cell lines. An important facet of …
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Background Single-cell transcriptomics is rapidly advancing our understanding of the
cellular composition of complex tissues and organisms. A major limitation in most analysis …
cellular composition of complex tissues and organisms. A major limitation in most analysis …
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 …
Experimental considerations for single-cell RNA sequencing approaches
QH Nguyen, N Pervolarakis, K Nee… - Frontiers in cell and …, 2018 - frontiersin.org
Single-cell transcriptomic technologies have emerged as powerful tools to explore cellular
heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell …
heterogeneity at the resolution of individual cells. Previous scientific knowledge in cell …
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network
Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous
classification of thousands of cells in a single assay based on transcriptome profiling. In …
classification of thousands of cells in a single assay based on transcriptome profiling. In …
[HTML][HTML] Identifying cell populations with scRNASeq
TS Andrews, M Hemberg - Molecular aspects of medicine, 2018 - Elsevier
Single-cell RNASeq (scRNASeq) has emerged as a powerful method for quantifying the
transcriptome of individual cells. However, the data from scRNASeq experiments is often …
transcriptome of individual cells. However, the data from scRNASeq experiments is often …
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] A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines
Single-cell transcriptomics (SCT) is a tour de force in the era of big omics data that has led to
the accumulation of massive cellular transcription data at an astounding resolution of single …
the accumulation of massive cellular transcription data at an astounding resolution of single …