Single-cell RNA-seq data analysis requires the use of several statistical methods and algorithms, which are often only accessible to users mastering computer tools such as R. We present an interactive graphical interface, which provides a guided, easy to use and comprehensive set of tools for single-cell RNA-seq data analysis, based on the R Shiny framework. The key steps of the application are: count data matrix import and normalization (CPM and TSV); primary exploratory analysis; differential gene expression; functional enrichment analysis; reporting. Raw data is imported in a simple text format. A Principal Component Analysis allows the detection of outliers and possible batch effects. Differential expression analysis can be performed using DESeq2 or edgeR with customisable parameters. The results can be visualised using: volcano-plot, heatmap and MA-plot. Functional pathway analysis can be done via Over-Representation Method, or via Gene Set Enrichment Analysis. Both methods, as well as all related visualisations make use of clusterProfiler R package, and are based on the complete set of MSigDB collection. Finally, a personalized HTML report can be created and downloaded. This Shiny application has been widely used in the framework of RNA-seq project analyses run by our bioinformatics core.