Statistical Analysis

Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore...

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to...

Genome-wide association studies provide a powerful means of identifying loci and genes contributing to disease, but in many cases, the related cell types...

Researchers from the University of Tsukuba have designed a statistical framework that identifies regulation of gene expression by neighboring cell...

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...

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...

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...

Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution...

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...

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...