Training data for "Clustering 3K PBMCs with Scanpy"

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 <em>et al.</em> 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 <strong>cellranger</strong> pipeline from 10X to extract an unique molecular identified (UMI) count matrix.