A Poisson reduced-rank regression model for association mapping in sequencing data
T Fitzgerald, A Jones, BE Engelhardt - BMC bioinformatics, 2022 - Springer
Background Single-cell RNA-sequencing (scRNA-seq) technologies allow for the study of
gene expression in individual cells. Often, it is of interest to understand how transcriptional …
gene expression in individual cells. Often, it is of interest to understand how transcriptional …
Detection of differentially expressed genes in discrete single-cell RNA sequencing data using a hurdle model with correlated random effects
M Sekula, J Gaskins, S Datta - Biometrics, 2019 - academic.oup.com
Single-cell RNA sequencing (scRNA-seq) technologies are revolutionary tools allowing
researchers to examine gene expression at the level of a single cell. Traditionally …
researchers to examine gene expression at the level of a single cell. Traditionally …
SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level
L Sun, G Wang, Z Zhang - Briefings in Bioinformatics, 2023 - academic.oup.com
Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful
technology for transcriptome analysis. However, the systematic validation of diverse …
technology for transcriptome analysis. However, the systematic validation of diverse …
Comparison and evaluation of statistical error models for scRNA-seq
S Choudhary, R Satija - Genome biology, 2022 - Springer
Background Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple
sources, including biological variation in cellular state as well as technical variation …
sources, including biological variation in cellular state as well as technical variation …
[PDF][PDF] RZIMM-SCRNA: A REGULARIZED ZERO-INFLATED MIXTURE MODEL FRAMEWORK FOR SINGLE-CELL RNA-SEQ DATA
D CANOLL, J HU - THE ANNALS, 2024 - imstat.org
Applications of single-cell RNA sequencing in various biomedical research areas have been
blooming. This new technology provides unprecedented opportunities to study disease …
blooming. This new technology provides unprecedented opportunities to study disease …
Pre-processing, dimension reduction, and clustering for single-cell RNA-seq data
The advent of scRNA-seq technologies enables us to quantitatively characterize gene
expression at each single-cell level. The high resolution has thus far transformed many …
expression at each single-cell level. The high resolution has thus far transformed many …
f-scLVM: scalable and versatile factor analysis for single-cell RNA-seq
Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression
in large cell populations. Such heterogeneity can arise due to technical or biological factors …
in large cell populations. Such heterogeneity can arise due to technical or biological factors …
Differential expression analysis for RNAseq using Poisson mixed models
Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is
among the most common analyses in genomics. However, RNAseq DE analysis presents …
among the most common analyses in genomics. However, RNAseq DE analysis presents …
Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data
Background Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at
the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize …
the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize …
[HTML][HTML] Leveraging gene correlations in single cell transcriptomic data
BACKGROUND: Many approaches have been developed to overcome technical noise in
single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for …
single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data—looking for …