Statistical Analysis

As the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available...

Single-cell RNA sequencing (scRNA-seq) technologies allow researchers to uncover the biological states of a single cell at high resolution. For computational efficiency and easy visualization...

RNA-seq studies are growing in size and popularity. Researchers from INSERM University of Bordeaux provide evidence that the most commonly used methods for differential expression analysis (DEA) may...

Single-cell RNA-sequencing (scRNA-seq) is becoming indispensable in the study of cell-specific transcriptomes. However, in scRNA-seq techniques, only a small fraction of the genes are captured due to “dropout” events. These dropout events require...

Single cell RNA sequencing (scRNA-seq), which promises to enable the quantitative study of biological processes at the single cell level, are now a routine part of experimental practice. One key computational challenge in analyzing scRNA-seq data is cell type annotation. ...

Single-cell RNA-sequencing (scRNA-seq) technology is a powerful tool to study organism from a single cell perspective and explore the heterogeneity between cells. Clustering is a fundamental step in scRNA-seq data analysis and it is the key to understand...

Variant allele frequencies (VAF) are an important measure of genetic variation that can be estimated at single-nucleotide variant (SNV) sites. RNA and...

Unsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore...

Transcript’s abundance is typically considered a proxy of the corresponding genes transcriptional activity. Yet, a poorly transcribed gene could see many of its RNA molecules accumulate just because they are highly stable. Conversely, if a gene is very actively transcribed ...

Gene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as...