A Novel Method to Identify the Differences Between Two Single Cell Groups at Single Gene, Gene Pair, and Gene Module Levels.

Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.

Differential correlation analysis of glioblastoma reveals immune ceRNA interactions predictive of patient survival.

BACKGROUND: Recent studies illuminated a novel role of microRNA (miRNA) in the competing endogenous RNA (ceRNA) interaction: two genes (ceRNAs) can achieve coexpression by competing for a pool of common targeting miRNAs. Individual biological investigations implied ceRNA interaction performs crucial oncogenic/tumor suppressive functions in glioblastoma multiforme (GBM). Yet, a systematic analysis has not been conducted to explore the functional landscape and prognostic significance of ceRNA interaction. RESULTS: Incorporating the knowledge that ceRNA interaction is highly condition-specific and modulated by the expressional abundance of miRNAs, we devised a ceRNA inference by differential correlation analysis to identify the miRNA-modulated ceRNA pairs. Analyzing sample-paired miRNA and gene expression profiles of GBM, our data showed that this alternative layer of gene interaction is essential in global information flow. Functional annotation analysis revealed its involvement in activated processes in brain, such as synaptic transmission, as well as critical tumor-associated functions. Notably, a systematic survival analysis suggested the strength of ceRNA-ceRNA interactions, rather than expressional abundance of individual ceRNAs, among three immune response genes (CCL22, IL2RB, and IRF4) is predictive of patient survival. The prognostic value was validated in two independent cohorts. CONCLUSIONS: This work addresses the lack of a comprehensive exploration into the functional and prognostic relevance of ceRNA interaction in GBM. The proposed efficient and reliable method revealed its significance in GBM-related functions and prognosis. The highlighted roles of ceRNA interaction provide a basis for further biological and clinical investigations.