UCLA researchers have developed an “all-in-one,” next-generation statistical simulator capable of assimilating a wide range of information to generate realistic synthetic data and provide a benchmarking tool for medical and biological researchers who use advanced...
Read More »Improving an rRNA depletion protocol with statistical design of experiments
In prokaryotic RNA-seq library preparation, rRNA depletion is required to remove highly abundant rRNA transcripts from total RNA. rRNA is so...
Read More »Significance analysis for clustering with single-cell RNA-sequencing data
Unsupervised clustering of single-cell RNA-sequencing data enables the identification and discovery of distinct cell populations. However, the most widely used clustering algorithms are heuristic and do not formally account for statistical uncertainty...
Read More »Current statistical approaches and outstanding challenges to differential expression analysis of single-cell RNA-seq data
With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated ...
Read More »scDLC – a deep learning framework to classify large sample single-cell RNA-seq data
Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have...
Read More »TWO-SIGMA-G – a new competitive gene set testing framework for scRNA-seq data accounting for inter-gene and cell–cell correlation
Researchers at Harvard T.H. Chan School of Public Health and the University of North Carolina at Chapel Hill propose TWO-SIGMA-G, a competitive gene set...
Read More »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...
Read More »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 ...
Read More »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, ...
Read More »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...
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