We published 3 protocols illustrating how MetaNeighbor can be used to quantify cell type replicability across single cell transcriptomic datasets.
The data files included here are needed to run the R version of the protocols available on Github (https://github.com/gillislab/MetaNeighbor-Protocol) in RMarkdown (.Rmd) and Jupyter (.ipynb) notebook format. To run the protocols, download the protocols on Github, download the data on Figshare, place the data and protocol files in the same directory, then run the notebooks in Rstudio or Jupyter.
The scripts used to generate the data are included in the Github directory. Briefly:
- full_biccn_hvg.rds contains a single cell transcriptomic dataset published by the Brain Initiative Cell Census Network (in SingleCellExperiment format). It combines data from 7 datasets obtained in the mouse primary motor cortex (https://www.biorxiv.org/content/10.1101/2020.02.29.970558v2). Note that this dataset only contains highly variable genes.
- biccn_hvgs.txt: highly variable genes from the BICCN dataset described above (computed with the MetaNeighbor library).
- biccn_gaba.rds: same dataset as full_biccn_hvg.rds, but restricted to GABAergic neurons. The dataset contains all genes common to the 7 BICCN datasets (not just highly variable genes).
- go_mouse.rds: gene ontology annotations, stored as a list of gene symbols (one element per gene set).
- functional_aurocs.txt: results of the MetaNeighbor functional analysis in protocol 3.