/Maier_et_al_nature_2020

This repository contains scripts used for analysis and figure generation in Maier, B., Leader, A.M., Chen, S.T. et al. A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580, 257–262 (2020). https://doi.org/10.1038/s41586-020-2134-y

Primary LanguageR

NOTE: Clustering and analysis of associated human lung cancer dataset will be reported in partner publication Leader, et al. (submitted as of 5/19/20).

All mice sequencing data are publicly available (GEO accession code GSE131957). All human sequencing data is available on NCBI with BioProject ID PRJNA609924.

Downloading the data

Data can either be downloaded automatically by running the script to reproduce the figures (see below). Alternatively, .rd files can be downloaded using the following dropbox links:

mouse scRNAseq data: https://www.dropbox.com/s/3ffq3z75af37rgr/mouse_dc.rd?dl=1

human DC scRNA & CITEseq data: https://www.dropbox.com/s/e0cvefqzoy4hz0r/human_dc.rd?dl=1

These links will download R data structures, the components of which contain the count matrices and cell metadata.

For the human DC dataset, the data structure is called "human_dc" and has the following components:

  1. filtered_umitab is the counts table
  2. cell_to_tissue, cell_to_sample, cell_to_annot, cell_to_patient are arrays with metadata identifying the tissue, sample, and patient of origin as well as our annotation (cDC1, cDC2, mregDC)
  3. filtered_ds is a downsampled matrix, where we have randomly drawn 2000 UMI from each cell containing at least that many. We use this sampling of UMI as a normalization for visualization that doesn't cause artifacts.
  4. adt_matrix_by_sample is a list of CITEseq ADT matrices for the samples that had CITEseq. We recommend within-patient normalization for analyses.

Making the figures

Requirements

Tested on Windows 10

  1. R

  2. R packages:

    • gplots
    • Matrix.utils
    • mixtools
    • seriation
    • sp
    • scales
    • scDissector

  3. Downloaded and unzipped version of this repository on a local path.

Running the scripts in R

Assuming Maier_et_al_nature_2020 is the local path of the repository we need to load the script files:

source("figures_main.R")`

Output

The above referenced dropbox links will download automatically to a new /data/ directory.

Figure will be generated in a new directory:

  • figures_out/

Clustering

Requirements

Tested on linux LSF HPC. Due to lack of support of some of the depdendencies, the script cannot run on macOS or Windows.

  1. R
  2. R packages:
  3. Downloaded and unzipped version of this repository on a local path.

Running the scripts in R Assuming Maier_et_al_nature_2020 is the local path of the repository, the following script will run the clustering distributedly on LSF:

source("clustering/run_clustering_mouse.r")

Note: Each run of the clustering might produce slightly different results due to different random seeds.