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Figures
README.md
R_basics.Rmd
analysis_template.Rmd
code_of_conduct.md
downloadData.R
exploration.Rmd
pharmaco_correlation.Rmd
rawPharmacoData.csv
regression.Rmd
summarizedPharmacoData.csv
targeted_therapies.Rmd

README.md

PR2017replicaton

Clone or download this repository to your computer and open the .Rmd files in Rstudio.

Main Analysis Template R Markdown:

  • analysis_template.Rmd : R markdown template which each team will use to create a fully reproducible analysis with the goal of assessing and interpreting the replicability of two pharmacogenomic experiments. This document will contain all of the text and code of their analyses, which are quided by a series of questions. The tools and concepts needed to answer the questions are explored in the tutorials.

Datasets:

  • rawPharmacoData.csv : raw data file generated by downloadData.R that contains drug response data at every dose and for each cell line and drug used in both studies.

  • summarizedPharmacoData.csv : summarized data file generated by downloadData.R that contains drug response data (combined over all doses) for each cell line and drug used in both studies.

Tutorials:

Each tutorial contains text and code that explores various aspects of data science, replicability, and reproducibility. Tutorial 0 provides a gentle introduction to R for those with limited programming experience. Tutorials 1-4 contain questions to fill in, some require written answers and some require code to produce a plot or numerical summary.

  • R_basics.Rmd : R markdown tutorial 0 - "R Basics"

  • exploration.Rmd : R markdown tutorial 1 - "What to do when you first get data"

  • pharmaco_correlation.Rmd : R markdown tutorial 2 - "Evaluating replicability of large pharmacological studies"

  • targeted_therapies.Rmd : R markdown tutorial 3 - "Identifying biological factors that influence replicability"

  • regression.Rmd : R markdown tutorial 4 - "Regression approaches for summarizing drug response data"

Code to generate data files (You do not need to use this):

For full reproducibility, this is the script to generate the csv files. This step has already been done for you.

  • downloadData.R : a script that uses the PharmacoGx package to format two datasets (raw and summary level) and save them as CSV files

Useful Links: