Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose–response study designs
The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and...
Representation learning of RNA velocity reveals robust cell transitions
RNA velocity is a promising technique for quantifying cellular transitions from single-cell transcriptome experiments and revealing transient cellular dynamics among a heterogeneous cell population. However, the cell transitions estimated from high-dimensional RNA velocity are often unstable or inaccurate, partly due ...
Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.
Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, ...
Powerful eQTL mapping through low coverage RNA sequencing
Mapping genetic variants that regulate gene expression (eQTLs) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants...
Genetic demultiplexing of pooled single-cell RNA-sequencing samples in cancer facilitates effective experimental design
Pooling cells from multiple biological samples prior to library preparation within the same single-cell RNA sequencing experiment provides several advantages, including lower library preparation costs and...
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis
Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool ...
On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiment
In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have...
Statistical analysis of differential transcript usage following Salmon quantification
Detection of differential transcript usage (DTU) from RNA-seq data is an important bioinformatic analysis that complements differential gene expression analysis. Researchers at the University of North Carolina at Chapel Hill have developed a simple workflow...
SAVER – gene expression recovery for single-cell RNA sequencing
In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable...
Gene expression distribution deconvolution in single-cell RNA sequencing
Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the...
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