Statisitcal Analysis

Understanding the functional impact of genomic variants is a major goal of modern genetics and personalized medicine. Although many synonymous and non-coding variants act through altering the...

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high...

St. Jude Children’s Research Hospital investigators have developed a software package to help identify biomarkers that differentiate between cell...

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq ...

Droplet based single cell transcriptomics has recently enabled parallel screening of tens of thousands of single cells. Clustering methods that scale for such high dimensional data without compromising accuracy are scarce. We exploit Locality Sensitive Hashing, an approximate nearest neighbour ...

Gene expression levels exhibit stochastic variations among genetically identical organisms under the same environmental conditions. In many recent transcriptome analyses based on RNA sequencing...

Batch effects are one of the major source of technical variations that affect the measurements in high-throughput studies such as RNA sequencing...

Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major ...

It is well known that batch effects exist in RNA-seq data and other profiling data. Although some methods do a good job adjusting for batch effects...

Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson ...