Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor
Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover
and characterize cell types; however, the experiment-specific technical biases and analytic …
and characterize cell types; however, the experiment-specific technical biases and analytic …
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 …
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 …
Probabilistic cell typing enables fine mapping of closely related cell types in situ
Understanding the function of a tissue requires knowing the spatial organization of its
constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has …
constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has …
[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 …
[HTML][HTML] Comprehensive integration of single-cell data
Single-cell transcriptomics has transformed our ability to characterize cell states, but deep
biological understanding requires more than a taxonomic listing of clusters. As new methods …
biological understanding requires more than a taxonomic listing of clusters. As new methods …
Jointly defining cell types from multiple single-cell datasets using LIGER
High-throughput single-cell sequencing technologies hold tremendous potential for defining
cell types in an unbiased fashion using gene expression and epigenomic state. A key …
cell types in an unbiased fashion using gene expression and epigenomic state. A key …
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 …
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied
to experiments representing a single condition, technology, or species to discover and …
to experiments representing a single condition, technology, or species to discover and …