MouseCortex

Welcome to the GitHub repository associated with our recent publication
Single-cell transcriptomic analysis of mouse neocortical development
Loo, Simon et al., Nature Communications, 2019: https://www.nature.com/articles/s41467-018-08079-9

Abstract

The development of the mammalian cerebral cortex depends on careful orchestration of proliferation, maturation, and migration events, ultimately giving rise to a wide variety of neuronal and non-neuronal cell types. To better understand cellular and molecular processes that unfold during late corticogenesis, we perform single-cell RNA-seq on the mouse cerebral cortex at a progenitor driven phase (embryonic day 14.5) and at birth—after neurons from all six cortical layers are born. We identify numerous classes of neurons, progenitors, and glia, their proliferative, migratory, and activation states, and their relatedness within and across age. Using the cell-type-specific expression patterns of genes mutated in neurological and psychiatric diseases, we identify putative disease subtypes that associate with clinical phenotypes. Our study reveals the cellular template of a complex neurodevelopmental process, and provides a window into the cellular origins of brain diseases.

What you'll find here

class.R: Additions and modifications to Shekhar et al.'s class.R file inititally published here
E14_processing.R: A detailed, fully documented summary of the processing steps performed for the E14.5 cortex.
- Requires class.R to access the functions necessary for data analysis
- All processing steps and parameters for analysis of the P0 cortex were identical to those illustrated in this template
E14_combined_matrix.txt.gz: The main gene expression matrix for E14.5 cortex.
- Rows are genes, columns are cells.
- Each biological replicate is represented here.
E14_tSNE.py: Code for visualizing the data using t-SNE, in Python
P0_combined_matrix.txt.gz: The main gene expression matrix for P0 cortex.
- Rows are genes, columns are cells.
- Each biological replicate is represented here.
E14_combined_matrix_ClusterAnnotations.txt and P0_combined_matrix_ClusterAnnotations.txt: Lookup tables with cell IDs and final cluster labels

We also created web-based tools for visualizing our data, these can be found at: http://zylkalab.org/data

Required R libraries

genefilter
sva
igraph
ggplot2
Matrix
gmodels
RANN
reshape
cluster
SLICER
gplots

Required Python libraries (for t-SNE visualization)

scikit-learn
matplotlib
pandas

Change log compared to Shekhar version:

Added several new slots to S4 object, including pca.eigenvalues, sils, and numclust
Added new function called perform_refined_clustering, which iteratively runs doGraph_clustering to optimize the number of nearest neighbors, then iteratively runs doGraph_clustering again to refine the cluster assignments using computed Silhouette widths
Computes eigenvalues as part of doPCA and stores these values for the permutation test
Adds a legend to t-SNE plots
Modifies the binomcount.test function to compute log-fold-change as (x+1)/(y+1) to avoid NA/Inf values
Adds checks to the binomcount.test to ensure that effect.size isn't NA
Modifies cluster naming on dot.plot
Prevents the usage of "Dingbats" font class in ggplot calls

Questions/Issues

Please direct any questions or issues with the code published here to jeremy [underscore] simon (at) med [dot] unc [dot] edu