pharyngeal_endoderm_development

This repository contains scripts for our paper entitled "Integration of single-cell transcriptomes and chromatin landscapes reveals regulatory programs driving pharyngeal organ development".

You can find the data at GEO: GSE182135 (scRNA atlas), GSE182136 (scRNA knockout experiment), and GSE182134 (scATAC data).

The repository is broadly organized as follows. Each directory requires a different set of packages as documented.

• scRNA_analysis

  a) atlas, foxn1_ko and interface_with_pseudotime_pouch_subset_analysis Contains code for Fig 1, 5, 6b and 6c

    Package requirements:
        - Seurat version 2 (from the Satija lab) 
        - thymusatlastools2 available at https://github.com/maehrlab/thymusatlastools2
        - https://github.com/ekernf01/freezr  to save code and session info and to track processed data for use in downstream scripts.
        - packrat (for dependency management)
        - magrittr
        - Matrix
  
   How to run:
   - in main.R edit the packrat(optional, can be deleted)
   - in main.R set the proj_dir to point to this repo
   - edit the flashfreeze paths to point to the location of the freezr and thymusatlastools2 packages
   - in setup.Rmd, edit the PATH_TO_DATA to point to the  CellRanger output directory (from GEO)
  

  b) pouch_object_pseudotime uses output of interface_with_pseudotime_pouch_subset_analysis Fig6a and FigS6; output used in 6b and 6c

   Package requirements:
    - scanpy (https://github.com/theislab/scanpy)
  
    How to run:
    - self contained jupyter notebook
  

  c) The standalone script FigS2_dotplot.R is used to make the dotplot in supplementary figure 2.

    Package requirements: Seurat version3, ggplot2 and cowplot
  

  d) notebooks "Fig6e_EnrichR_interface" and "Fig6e_dotplot_from_curated_terms" are used to make the dotplots in Fig 6

    Package requirements : enrichR, dplyr, magrittr, ggplot2, viridis, scales
  

• scATAC_qc_analysis Contains code for Fig 2, 3, 5

    Package requirements:
    - ArchR (ArchR_1.0.1 in R 3.6.3)
    - MACS2 (part of ArchR suggested packages)
    - Cis-BP version 2 database (http://cisbp.ccbr.utoronto.ca)
    - Seurat - version 3
    - scanpy (https://github.com/theislab/scanpy)
    - rGREAT (https://www.bioconductor.org/packages/release/bioc/html/rGREAT.html)
    - universalmotif (https://bioconductor.org/packages/release/bioc/html/universalmotif.html)
    - TFBSTools (https://bioconductor.org/packages/release/bioc/html/TFBSTools.html)
    - ChromVar (part of ArchR suggested packages)
    - lineup (https://github.com/kbroman/lineup)
    - Phastcon score download (http://hgdownload.cse.ucsc.edu/goldenpath/mm10/phastCons60way/mm10.60way.phastCons60wayEuarchontoGlire.bw)
    - enrichR (https://github.com/wjawaid/enrichR)
    - jupyter
    
    How to run:
    - series of self contained jupyter notebooks and R scripts
  

• gene_regulatory_networks Contains code for Fig 4, Fig 5a and Fig 5e NOTE: Code for creating the metacells is in the scRNA_analysis directory

  a) GENIE3 network (run from SCENIC package)

    Package requirements:
    - R version (R 3.6.3)
    - metacell_0.3.6
    - Seurat (v3)
    - Matrix
    - magrittr
    - parallel
    - RcisTarget
    - GENIE3
    - SCENIC_1.2.4 
    - igraph
    - network
    - sna
    - visNetwork
    - leiden
    - ggplot2
    NOTE: Heatmap in Fig 4e was made with scanpy, panda and anndata
  
  How to run:
      - series of self contained jupyter notebooks which also contain further details on packages used
  

  b) CellOracle GRN

    Package requirements:
    - CellOracle 0.6.6 and all the prescribed dependencies
    - scvelo 0.2.2.dev51+ga7de78a and all the prescribed dependencies
    NOTE: Fig 5e was made using R3.6.3, ggplot2 and Seurat
  
    How to run:
    - series of self contained jupyter notebooks which also contain further details on packages used