Spatially resolved transcriptomics technologies enable the measurement of transcriptome
information while retaining the spatial context at the regional, cellular or sub-cellular level …

Microarray technology facilitates the simultaneous measurement of expression of tens of
thousands of genes and enables us to study cancers and tumors at the molecular level …

Exploring the chemical content of individual cells not only reveals underlying cell‐to‐cell
chemical heterogeneity but is also a key component in understanding how cells combine to …

Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations
is crucial for delineating the underlying mechanisms driving biological or clinical …

For many high-dimensional genomic and epigenomic datasets, the outcome of interest is
ordinal. While these ordinal outcomes are often thought of as the observed cutpoints of …

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to
interpret similarities and differences between single-cell samples. Although different batch …

Motivation Understanding single-cell expression variability (scEV) or gene expression noise
among cells of the same type and state is crucial for delineating population-level cellular …

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-
cell RNA-seq data. Previous benchmarks used two different criteria to compare feature …

Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent
space with minimized defined optimization objectives. The two independent branches of DR …

Feature gene selection has significant impact on the performance of cell clustering in single-
cell RNA sequencing (scRNA-seq) analysis. A well-rounded feature selection (FS) method …