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.
- 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.
- 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 pullto obtain the materials for this class, it is likely because you have a conflict betweenLecture18-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, likemy_Lecture18-scRNA-seq-analysis.ipynb)) and then discarding changes to the original markdown file.
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.
Please use the environment tfcb2022_rna which has all dependencies installed.