scID: identification of transcriptionally equivalent cell populations across single cell RNA-seq data using discriminant analysis
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 …
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 …
Identifying cell types to interpret scRNA-seq data: how, why and more possibilities
Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our
understanding of biological phenomena at the cellular scale. Identification of cell types has …
understanding of biological phenomena at the cellular scale. Identification of cell types has …
BCseq: accurate single cell RNA-seq quantification with bias correction
With rapid technical advances, single cell RNA-seq (scRNA-seq) has been used to detect
cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in …
cell subtypes exhibiting distinct gene expression profiles and to trace cell transitions in …
COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection
Background Single-cell RNA sequencing enables studying cells individually, yet high gene
dimensions and low cell numbers challenge analysis. And only a subset of the genes …
dimensions and low cell numbers challenge analysis. And only a subset of the genes …
One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can
aggregate complementary biological information from different datasets. However, most …
aggregate complementary biological information from different datasets. However, most …
Information-theory-based benchmarking and feature selection algorithm improve cell type annotation and reproducibility of single cell RNA-seq data analysis …
Single cell RNA sequencing (scRNA-seq), which promises to enable the quantitative study
of biological processes at the single cell level, are now a routine part of experimental …
of biological processes at the single cell level, are now a routine part of experimental …
ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions
Abstract Motivation Single cell RNA-Seq (scRNA-Seq) facilitates the characterization of cell
type heterogeneity and developmental processes. Further study of single cell profiles across …
type heterogeneity and developmental processes. Further study of single cell profiles across …
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 …
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 …