Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
M Su, T Pan, QZ Chen, WW Zhou, Y Gong, G Xu… - Military Medical …, 2022 - Springer
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has
advanced our understanding of the pathogenesis of disease and provided valuable insights …
advanced our understanding of the pathogenesis of disease and provided valuable insights …
Machine learning and statistical methods for clustering single-cell RNA-sequencing data
R Petegrosso, Z Li, R Kuang - Briefings in bioinformatics, 2020 - academic.oup.com
Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-
transcriptome profiling of each individual single cell in a cell population. A core analysis of …
transcriptome profiling of each individual single cell in a cell population. A core analysis of …
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments,
laboratories and technologies can uncover biological insights, but current methods for …
laboratories and technologies can uncover biological insights, but current methods for …
Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis
D Schafflick, CA Xu, M Hartlehnert, M Cole… - Nature …, 2020 - nature.com
Cerebrospinal fluid (CSF) protects the central nervous system (CNS) and analyzing CSF
aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains …
aids the diagnosis of CNS diseases, but our understanding of CSF leukocytes remains …
Clustering single-cell RNA-seq data with a model-based deep learning approach
Single-cell RNA sequencing (scRNA-seq) promises to provide higher resolution of cellular
differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq …
differences than bulk RNA sequencing. Clustering transcriptomes profiled by scRNA-seq …
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
Clustering is a critical step in single cell-based studies. Most existing methods support
unsupervised clustering without the a priori exploitation of any domain knowledge. When …
unsupervised clustering without the a priori exploitation of any domain knowledge. When …
An entropy-based metric for assessing the purity of single cell populations
Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating
cell types and states, but the determination and annotation of cell subtypes is often …
cell types and states, but the determination and annotation of cell subtypes is often …
Putative cell type discovery from single-cell gene expression data
Abstract We present the Single-Cell Clustering Assessment Framework, a method for the
automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) …
automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) …
[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 …
SAFE-clustering: single-cell aggregated (from ensemble) clustering for single-cell RNA-seq data
Motivation Accurately clustering cell types from a mass of heterogeneous cells is a crucial
first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several …
first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several …