Single cell RNA sequencing (scRNA-Seq) technology has revealed significant differences in gene expression levels between different cell groups. However, most of the studies focus on the analysis of mRNAs, ignore long non-coding RNAs (lncRNAs), which have been shown to be more abundant and have significant cell-specificity. In this study, we developed ColorCells
, a platform for comparative analysis of long non-coding RNAs (lncRNAs) and mRNAs expression, classification and functions in single-cell RNA-Seq data
. We apply ColorCells to analyze 167913 publicly available scRNA-Seq experiments from 5 species. Integrative annotation of lncRNAs reveals large numbers of cell-specific lncRNAs and their properties. We provides a serious of novel tools and friendly visual interface in ColorCells, including apply PCA and t-SNE algorithm to display
cell clusters in 2D and 3D space, develop a tissue map
tool to show various tissues and cell types in humans and mouse, establish a statistical test method for hypergeometric distribution to automatically assigncell type labels
to cell clusters, estimate cell-cell similarity
based on SNN and pearson correlation analysis, built protein-lncRNA co-expression networks to predict lncRNAs function
from scRNA-Seq data. Our study emphasizes the need to uncover lncRNAs that occur in all types of cells, and we wish ColorCells to be a good resource for revealing the features, expression and functions of lncRNAs in single cells.