Single-cell RNA sequencing (scRNA-seq) in liver cancer : software development and data analysis

Abstract

The application of single-cell RNA sequencing (scRNA-seq) to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, scRNA-seq offers an unprecedented research power. It is particularly useful in cancer research, where tumor tissue has a highly admixed cell population and hence is difficult to study. Although some popular software packages are available for scRNA-seq data analysis and visualization, it is still a big challenge for their usage, as they provide only command-line interface and require significant level of bioinformatics skills. On the other hand, several scRNA-seq analysis pipelines with graphical interactions are available, but they have limited functionalities. Hence, there is an urgent need for software packages with graphical user interface and comprehensive functionalities to facilitate scRNA-seq studies. We initiate our study to overcome this research problem. First, we have developed a scRNA-seq analysis tool called scAnalyzeR, which is a Shiny app under R environment with an interactive and user-friendly graphical interface for analyzing and visualizing scRNA-seq data. scAnalyzeR runs as a stand-alone program or on any modern web browsers through RStudio. It accepts scRNA-seq data from various technology platforms, e.g., 10X genomics, Fluidigm or SMART-seq, 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. With automated installation procedures for necessary libraries and the corresponding dependencies, and a graphical user-interface that access the comprehensive functionalities of the software tool, we anticipate ready application for users even with minimal programming knowledge. Next, we compared the performance of scAnalyzeR with two other graphical tools that are popular for analyzing scRNA-seq data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and the execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. Lastly, to illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer scRNA-seq dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Findings also suggest the existence of progenitor cell-like tumor cells residing within HCC tumor, and they were observed to have enriched expression for multiple stemness-related or liver CSC markers (e.g. EPCAM, MYC, CD24 and CD47). Further experimental verification is warranted to consolidate this hypothesis. We believe our study provides a novel and useful tool for scRNA-seq studies. It will likely contribute to the wide applications of scRNA-seq technology in different areas, including but not limited to developmental biology, immunology, cancer research and neuroscience.