Despite substantial progress in single-cell RNA-seq (scRNA-seq) data analysis methods,
there is still little agreement on how to best normalize such data. Starting from the basic …

The normalization of RNA-seq data is essential for accurate downstream inference, but the
assumptions upon which most normalization methods are based are not applicable in the …

Motivation Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite
to their interpretation. The marked technical variability, high amounts of missing …

Normalization of RNA-sequencing (RNA-seq) data has proven essential to ensure accurate
inference of expression levels. Here, we show that usual normalization approaches mostly …

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to
technical factors, including the number of molecules detected in each cell, which can …

Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique
molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase …

A major roadblock towards accurate interpretation of single cell RNA-seq data is large
technical noise resulted from small amount of input materials. The existing methods mainly …

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool to determine expression
patterns of thousands of individual cells. However, the analysis of scRNA-seq data remains …

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 variance. Current analysis …

Background Standard preprocessing of single-cell RNA-seq UMI data includes
normalization by sequencing depth to remove this technical variability, and nonlinear …