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 …
SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references
Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of
transcriptomic profiles with single-cell resolution and circumvent averaging artifacts …
transcriptomic profiles with single-cell resolution and circumvent averaging artifacts …
Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data
Background Single-cell RNA-sequencing (scRNA-seq) measures gene expression in single
cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin …
cells, while single-nucleus ATAC-sequencing (snATAC-seq) quantifies chromatin …
Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch …
With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-
seq) datasets, bioinformatics methods based on gene co-expression network are becoming …
seq) datasets, bioinformatics methods based on gene co-expression network are becoming …
scCAN: single-cell clustering using autoencoder and network fusion
Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important
because it allows us to identify putative cell types. However, the large number of cells (up to …
because it allows us to identify putative cell types. However, the large number of cells (up to …
scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication
Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data
promises further biological insights that cannot be uncovered with individual datasets …
promises further biological insights that cannot be uncovered with individual datasets …
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq)
data promises further biological insights that cannot be uncovered with individual datasets …
data promises further biological insights that cannot be uncovered with individual datasets …
Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data
KL Tiong, D Luzhbin, CH Yeang - BMC bioinformatics, 2024 - Springer
Background Single-cell RNA sequencing (sc-RNASeq) data illuminate transcriptomic
heterogeneity but also possess a high level of noise, abundant missing entries and …
heterogeneity but also possess a high level of noise, abundant missing entries and …
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 …
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 …