Protocol data (Python version)

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 Python version of the protocols available on Github (https://github.com/gillislab/MetaNeighbor-Protocol) in 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 Jupyter. Briefly:- biccn_hvg.h5ad contains a single cell transcriptomic dataset published by the Brain Initiative Cell Census Network (in AnnData 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_gaba.h5ad: same dataset as biccn_hvg.h5ad, but restricted to GABAergic neurons. The dataset contains all genes common to the 7 BICCN datasets (not just highly variable genes).- hemberg.h5ad contains a merged version of 4 human pancreas single cell transcriptomic datasets made available in a standardized form by the Hemberg lab.- tasic.h5ad contains a single cell transcriptomic dataset of neurons from the mouse primary visual cortex, as published in Tasic et al. 2016.- go_mouse.mtx, go_mouse_col_labels.txt, go_mouse_row_labels.txt: mouse gene ontology annotations, stored as a one-hot encoded matrix.