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Lectures 18: Introduction to single-cell RNA-seq

In this lecture, we will take analyze a single-cell RNA-seq data using scanpy. The lecture will introduce Anndata objects, plotting and interacting witn single-cell RNA-seq, QC and analysis of data and as time permits, batch correction.

We will use two PBMC datasets made available by 10X Genomics. Please download the following to the data/ directory:

  • Pre-analyzed data from here.
  • Count matrix from here. See here for description of the data.
  • A second count matrix of PBMCs to be used for batch-correction. Download from here and a description is available here.

Learning Objectives

  • Insights about why and how of single-cell RNA-seq.
  • Learn how to process and analyze single-cell RNA-seq datasets.
  • Single-cell RNA-seq data is highly interactive. Learn different ways to visualize and interact with the data.
  • Perform batch correction of scRNA-seq data.
  • Understand the reasoning behind various QC, preprocessing and analysis approaches for scRNA-seq.

Class materials

  • The lecture slides are available here
  • The Jupyter notebook which will be used for the lecture are available here slides are available Lecture18-scRNA-seq-analysis.ipynb. If you have difficulty performing a git pull to obtain the materials for this class, it is likely because you have a conflict between Lecture19-scRNA-seq-analysis.ipynb) and the version in the public GitHub repo. You can resolve this by making a copy of that markdown (naming it something different, like my_Lecture19-scRNA-seq-analysis.ipynb)) and then discarding changes to the original markdown file.

Data Download

Download the following datasets and copy it a folder called data/

  • Pre-analyzed data from here.
  • Count matrix from here. See here for description of the data.
  • A second count matrix of PBMCs to be used for batch-correction. Download from here and a description is available here.

Environment setup

Please use the environment tfcb2022_rna which has all dependencies installed.