  Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity
in cells, which has given us insights into cell-cell communication, cell
differentiation, and differential gene expression. However, analyzing scRNA-seq
data is a challenge due to sparsity and the large number of genes involved.
Therefore, dimensionality reduction and feature selection are important for
removing spurious signals and enhancing downstream analysis. Correlated
clustering and projection (CCP) was recently introduced as an effective method
for preprocessing scRNA-seq data. CCP utilizes gene-gene correlations to
partition the genes and, based on the partition, employs cell-cell interactions
to obtain super-genes. Because CCP is a data-domain approach that does not
require matrix diagonalization, it can be used in many downstream machine
learning tasks. In this work, we utilize CCP as an initialization tool for
uniform manifold approximation and projection (UMAP) and t-distributed
stochastic neighbor embedding (t-SNE). By using eight publicly available
datasets, we have found that CCP significantly improves UMAP and t-SNE
visualization and dramatically improve their accuracy.
