This notebook is described in https://f1000research.com/articles/7-1306/v2
The goal of this notebook is to provide a standard single-cell RNA-seq analysis workflow for pre-processing, identifying sub-populations of cells by clustering, and exploring biomarkers to explain intra-population heterogeneity. The workflow is modeled after the Seurat Guided Clustering Tutorial and performs all analyses using the scanpy library.
This notebook identifies immune cell types by clustering single-cell RNA-seq data using R and Python to run non-negative matrix factorization.
This notebook uses STREAM a trajectory inference method to accurately reconstruct complex developmental trajectories. It also provides informative and intuitive visualizations to recover and highlight important genes that define subpopulations and cell types.