scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer.

Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.

Network Biology Approaches to Uncover Therapeutic Targets Associated with Molecular Signaling Pathways from circRNA in Postoperative Cognitive Dysfunction Pathogenesis.

Postoperative cognitive dysfunction (POCD) is a cognitive deterioration and dementia that arise after a surgical procedure, affecting up to 40% of surgery patients over the age of 60. The precise etiology and molecular mechanisms underlying POCD remain uncovered. These reasons led us to employ integrative bioinformatics and machine learning methodologies to identify several biological signaling pathways involved and molecular signatures to better understand the pathophysiology of POCD. A total of 223 differentially expressed genes (DEGs) comprising 156 upregulated and 67 downregulated genes were identified from the circRNA microarray dataset by comparing POCD and non-POCD samples. Gene ontology (GO) analyses of DEGs were significantly involved in neurogenesis, autophagy regulation, translation in the postsynapse, modulating synaptic transmission, regulation of the cellular catabolic process, macromolecule modification, and chromatin remodeling. Pathway enrichment analysis indicated some key molecular pathways, including mTOR signaling pathway, AKT phosphorylation of cytosolic targets, MAPK and NF-κB signaling pathway, PI3K/AKT signaling pathway, nitric oxide signaling pathway, chaperones that modulate interferon signaling pathway, apoptosis signaling pathway, VEGF signaling pathway, cellular senescence, RANKL/RARK signaling pathway, and AGE/RAGE pathway. Furthermore, seven hub genes were identified from the PPI network and also determined transcription factors and protein kinases. Finally, we identified a new predictive drug for the treatment of SCZ using the LINCS L1000, GCP, and P100 databases. Together, our results bring a new era of the pathogenesis of a deeper understanding of POCD, identified novel therapeutic targets, and predicted drug inhibitors in POCD.