Pan-cancer analysis of proliferation rates and metabolic liabilities

Project to study proliferation and metabolic liabilities across several thousand cancer samples. The automatically generated output can be found at https://cdiener.github.io/proliferation

What this repository contains

This repository contains all additional files to fully reproduce all analyses. It uses a R markdown file to automatically create a detailed protocol of the analysis which is available at https://cdiener.github.io/proliferation.

Intermediate data

In case you would like to reproduce the analysis but do now want to wait downloading the raw data we also provide some intermediate data files you can (optinonally) use to accelerate the analysis:

1. NCI-60 gene expression data and proliferation rates http://dx.doi.org/10.5281/zenodo.61980
2. TCGA data as compressed RDS file http://dx.doi.org/10.5281/zenodo.61982
3. probe map as compressed RDS file Already in the Github repository

Proliferation rate predictions

The predicted proliferation rates for more than 12000 samples from the TCGA.

1. Raw predictions for 12339 samples together with their respective barcode and cancer panel. results/pred_rates.csv
2. Raw predictions for 12111 samples that had clinical annotations merged with several clinical indicators such as survival data, histology types, TNM stage, follow ups and no. of new tumor events. results/combined.csv

Rerunning the analysis interactively

Locally

Note that this will require a machine with 16+ GB of RAM.

If you have docker installed you can use the steps provided in the section below "With a cloud provider".

For a local installation you will need R (http://r-project.org), git (http://git-scm.org) and Python 3 installed (http://python.org).

On Ubuntu or Debian Stretch these can be installed via

sudo apt-get install r-base r-base-dev python3 python3-pip git

Clone the repository and enter the folder:

git clone https://github.com/cdiener/proliferation && cd proliferation

For Debian Jessie we recommend updating pip on a per-user setting in order to get a version of pip that is greater than 8.1.

sudo apt-get install r-base r-base-dev python3 python3-pip git
pip3 install -U pip

The python dependencies can be installed with

pip3 install lxml python-libsbml numpy scipy cobra

R dependencies can be installed from within R (type "R" in Terminal) with

install.packages("devtools")
devtools::install_github("cdiener/proliferation/prtools")

You can now run the steps presented in the protocol.

With a cloud provider

Using a cloud provider such as Google Cloud or Amazon AWS you can rerun the analysis easily with the provided docker image.

1. Create a new virtual machine with more than 16 GB of RAM using the CoreOS stable image.

2. Login to the machine using SSH as described by your cloud provider.

3. Get the docker image with

   docker pull cdiener/proliferation

4. Run the docker image

   docker run -d -p 8000:8787 cdiener/proliferation

   (You can now disconnect the SSH connection, but leave the VM running.)

5. Access the machine at http://your-ip:8000 where "your-ip" is the IP of your VM or "localhost" when running docker on your own machine. You will be prompted with for login information where the user and password are "rstudio".

This will present you with an R studio interface where all additional dependencies and intermediate data are available.

For a start click the "..." symbol in the file panel on the lower right and enter "/data/proliferation". Now click on "protocol.rmd" to see or run the protocol (by clicking "knit HTML") or use any of the *.R files in the same directory.