RNA-Seq Blog Transcriptome Research & Industry News
Researchers from the University of Tsukuba have designed a statistical framework that identifies regulation of gene expression by neighboring cell...
Read More »Neighboring cell types influence single-cell gene expression variability
CellRegMap – a statistical framework for mapping context-specific regulatory variants using scRNA-seq
Single-cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Recent technological advances have enabled the first population-scale scRNA-seq studies in...
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 »Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality
The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a...
Read More »scISR – a novel method for single-cell data imputation using subspace regression
Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution...
Read More »ResPAN – a powerful batch correction model for scRNA-seq data through residual adversarial networks
With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However...
Read More »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 millions), the high...
Read More »MURP – a downsampling method that enables robust clustering and integration of single-cell transcriptome data
The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in...
Read More »baredSC – Bayesian approach to retrieve expression distribution of single-cell data
The number of studies using single-cell RNA sequencing (scRNA-seq) is constantly growing. This powerful technique provides a sampling of the whole transcriptome of a cell. However, sparsity of the data can be a major hurdle when studying the...
Read More »scISR – a novel method for single-cell data imputation using subspace regression
Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution...
Read More »