This repository contains code for reproducing the analysis of snRNA-seq data from human fronto-insula described in Hodge, Miller, et al 2020.
While this is set up as an R library for convenience, it is really just a wrapper for the analysis functions and does not follow all of the conventions of R libraries (function annotation, unit testing, etc.). Please be sure to cite the appropriate other R libraries if replicating the code.
Install using:
devtools::install_github("AllenInstitute/VENcelltypes")
There are several dependencies listed in the code books below, but one notable one is that version 3.0 of Seurat needs to be installed.
devtools::install_github(repo = "satijalab/seurat", ref = "release/3.0")
To replicate the figures in the paper, please use the code below:
- Code 1. Download and prepare the data (LINK TO SCRIPT) This script reads converts data downloaded from the Allen Institute Cell Types Database into a format compatible for use as comparison data to human fronto-insula.
- Code 2. Rename clusters and build tree (LINK TO SCRIPT) This script reads in the data and assigned clusters, organizes the clusters into a dendrogram, renames the clusters to include class information and informative genes, and then plots some summary plots.
- Code 3. Quality control assessment (LINK TO SCRIPT) This script reads in the data output to the feather files and uses it to calculate and plot some QC information for Supplementary figure 1.
- Code 4. Cross-species analysis (LINK TO SCRIPT) This script performs alignment of deep excitatory neurons in human fronto-insula, human middle temporal gyrus, mouse primary visual cortex, and mouse anterior lateral motor cortex. It also identifies genes with common and distinct patterning across these data sets.
- Code 5. Human FI vs. Human MTG comparison (LINK TO SCRIPT) This script identifies common and distinct CF-associated genes in human middle temporal gyrus as compared with human fronto-insula, and compares the proportions of cells across clusters for each brain region.
All code was tested in both a UNIX and Windows environment and show to produce the same result, with the exception of the UMAP visualization noted in Code 4.
Please e-mail [email protected] with any issues.
The license for this package is available on Github at: https://github.com/AllenInstitute/VENcelltypes/blob/master/LICENSE
This code will be updated only if figures change during review.
If you contribute code to this repository through pull requests or other mechanisms, you are subject to the Allen Institute Contribution Agreement, which is available in full at: https://github.com/AllenInstitute/VENcelltypes/blob/master/CONTRIBUTION