Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq. In this tutorial, we will investigate clustering of single-cell data from 10x Genomics, including preprocessing, clustering and the identification of cell types via known marker genes, using Scanpy (Wolf et al. 2018). It is illustrated using a dataset of Peripheral Blood Mononuclear Cells (PBMC), extracted from a heal, freely available from 10X Genomics. The dataset contains 2,700 single cells sequencd using Illumina NextSeq 500. The raw sequences have been processed by cellranger pipeline from 10X to extract an unique molecular identified (UMI) count matrix.