ScRecover – discriminating true and false zeros in single-cell RNA-seq data for imputation
Researchers analyze the expression of genes in individual cells to understand cellular functions and behaviors. However, one challenge researchers face with scRNA-seq data is the presence of many "zero" values—indicating either that a gene is not being expressed in ...
scGFT – single-cell RNA-seq data augmentation using generative Fourier transformer
Single-cell RNA sequencing (scRNA-seq) offers an unprecedented opportunity to understand the cellular complexity and heterogeneity that defines both healthy and diseased states. By measuring the gene expression profiles of individual cells, researchers can uncover the complex workings of different ...
A benchmark of RNA-seq data normalization methods for transcriptome mapping
Metabolism is the set of life-sustaining chemical reactions in organisms that allows them to grow, reproduce, maintain their structures, and respond to their environments. To study how these metabolic processes change in different conditions, scientists use genome-scale metabolic models ...
The effect of data transformation on low-dimensional integration of single-cell RNA-seq
Recent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular populations. However...
New in silico benchmarking tool to evaluate and validate single-cell RNA-Seq and spatial transcriptomics computational methods
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...
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...
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...
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 ...
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...
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...
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