cbcrg/mouse-pergola-reproduce

Name: mouse-pergola-reproduce

Owner: Notredame Lab

Description: Mouse dataset Pergola paper analysis

Forked from: JoseEspinosa/mouse-pergola-reproduce

Created: 2017-11-28 09:21:48.0

Updated: 2017-11-28 09:21:54.0

Pushed: 2018-01-14 12:07:38.0

Homepage: null

Size: 10547

Language: R

GitHub Committers

User	Most Recent Commit	# Commits

Other Committers

User	Email	Most Recent Commit	# Commits

README

mouse-pergola-reproduce.nf

This repository contains the software, scripts and data to reproduce the results corresponding to the CB1 mice experiment of the Pergola paper.

If you have not install yet docker and nextflow, follow this intructions

Clone the repository

clone --recursive https://github.com/cbcrg/mouse-pergola-reproduce.git
ouse-pergola-reproduce

Data

Data is publicly available in Zenodo as a compressed tarball .

Data can be downloaded and uncompressed using the following command:

r data
 -O- https://zenodo.org/record/1067839/files/mouse_dataset.tar.gz | tar xz -C data

Pull docker image

Pull the Docker image use for processing data with Pergola (Pergola and its dependencies installed)

er pull pergola/pergola@sha256:0737f3e70dde73d0272a0be37e05a36a6fed390c8820c5d2834076b711ab707f

Run nextflow pipeline

Once data is downloaded, it is possible to reproduce all the results using this command:

VER=0.26.4 nextflow run mouse-pergola-reproduce.nf \
recordings='data/mouse_recordings/' \
mappings='data/mappings/b2p.txt' \
mappings_bed='data/mappings/bed2pergola.txt' \
phases='data/phases/exp_phases.csv' \
mappings_phase='data/mappings/f2g.txt' \
exp_info='data/mappings/exp_info.txt' \
ith-docker

Results

The previous command generates a results folder that contains the results of the analysis shown in the paper:

• A figure created using Gviz displaying the mouse feeding behavior and mouse accumulated food intakes within 30 minutes time-windows displayed as a heatmap.
  
• A figure created using Sushi rendering the same layout as in the previous figure.
• A folder containing all the necessary files to render the raw feeding behavior and the the accumulated food intakes using IGV. Data is separated in folders corresponding to each mouse group.
• A plot comparing the preference for the high-fat group of the different mouse group.
• A folder named files which contains the complete set of files needed to visualize the data using Shiny-pergola as explained below.

Online Shiny-Pergola visualization

shiny-pergola config file

The repository includes a file assigning files to each of the group (WT food sc, WT nic food sc, CB1 food sc, CB1 nic food sc, WT food fat, WT nic food fat, CB1 food fat, CB1 nic food fat) You can also check the file here

Downloading and running the shiny-pergola image

Pull the docker image containing the version of shiny-pergola web application used for render the data visualization:

er pull pergola/shiny-pergola@sha256:e8791c5f230b612a6f702ac397849163e3a52b923befd1977e4a4c0235e91f72

With docker running, launch the image:

er run --rm -p 3600:80 -v "$(pwd)":/pergola_data  pergola/shiny-pergola@sha256:e8791c5f230b612a6f702ac397849163e3a52b923befd1977e4a4c0235e91f72 &

Note: "$(pwd)" can be substitute by your absolute path to the folder where the mouse-pergola-reproduce.nf has been run.

Note: Paper contain several figures related to mouse dataset analysis. If you want to get exactly one of the figure just select it by setting the figure on id.txt file. For instance if you want to reproduce Supplementary figure 4a you just have to type the following command before running Docker shiny-pergola image.

 "cb1_b" > id.txt

The codes for each figure are:

| Figure | Code | | ————- | —– | | Supp. Fig. 3 | cb1_a | | Supp. Fig. 4a | cb1_b | | Supp. Fig. 4b | cb1_c |

Go to your web browser and type in your address bar the ip address returned by the following command e.g. http://0.0.0.0:3600

Note: In newer Docker versions by default the IP address used is the localhost 0.0.0.0. As this IP might be used by other services in the default port (80), we especified the port used in our docker command by -p 3600:80. Old docker versions might used a different IP address that you can get using the following command (usually this one http://192.168.99.100). You may then enter this IP followed by the same port in the address bar of your browser e.g. http://192.168.99.100:3600.

er-machine ip default 

Et voila, the running container will load the shiny app in your browser.