Project 057

This repository contains R scripts to accompany: Ellender TJ, Avery SV, Mahfooz K, et al. Embryonic progenitor pools generate diversity in fine-scale excitatory cortical subnetworks. Nat Commun. 2019;10(1):5224. Published 2019 Nov 19. doi:10.1038/s41467-019-13206-1

Installation

1. Install https://github.com/cgat-developers/cgat-core and https://github.com/cgat-developers/cgat-apps
2. Install https://github.com/AllenInstitute/scrattch.hicat
3. Download the project057 github repository
4. Run `setup.py develop` in the project057 directory

Usage

The scripts require a `.tsv` counts table, generated e.g. by featurecounts as input. They also require an annotation table for the columns.

Run the cgat-singlecell --help command to see what scripts are available and how to use them.

For example, to run filter data obtained from featurecounts `cgat-singlecell filter --counts-filename=featurecounts.tsv --phenotypes-filename=phenodata.tsv --factor=group,mouse_id,collection_date,slice_depth,slice_number,pipette_visual,timepoint > filtered_counts.tsv`

To run the normalisation script and map the data onto a reference dataset (e.g. Allen) use: `cgat-singlecell normalisation --rds-filename sce_filtered_hicat.rds --ERCC ERCC.tsv --allen-design mouse_VISp_2018-06-14_samples-columns.csv --allen-datamatrix mouse_VISp_2018-06-14_exon-matrix.csv --allen-rowdata mouse_VISp_2018-06-14_genes-rows.csv --allen-filter "L4 IT" --norm scran --colours red,green --allen-colours black --perplexity 5 > pipeline.log`

Analysis Sequence

For the publication the following sequence was used on each experimental plate separately.

1. Filtering of datase using `cgat-singlecell filtering`
2. Run scrattch.hicat using wrapper script `cgat-singlecell hicat`
3. Normalisation and mapping to reference dataset using `cgat-singlecell normalisation`
4. Differential expression analysis using `cgat-singlecell sc-diffexpression`