Introduction

This repo is intended to contain code to reproduce results from the paper, False Discovery Rates: A New Deal.

The hardest part may be making sure R is set up with all the right packages installed. I have used the R package packrat to try to make this easier, but on some systems it may be easier to install all the packages you need by hand. If things go wrong, look also at "If things go wrong" below.

Step-by-step instructions

1. Preliminaries: I made the paper with R v3.2.3, so you might start by installing this version. Or more recent versions should (!) also work. Install pandoc v1.12.3 or higher. You will also need a working pdflatex installation to make the paper. The Rmosek package mosek needs to be installed, so do that now, too. Don't forget to follow instructions regarding the license file. Possibly you will come across other dependencies as you run the following steps.

2. Clone (or download and unzip) this repository.

3. Install the R packages you need. I have tried to use the packrat package to automate this process, with some measure of success. To do it this way, start up R (e.g. from the command line) within the repository directory. The first time you enter R the hidden .Rprofile file will cause R to try to install all the packages you need to a local library in the packrat subdirectory. (Specifically it should create a packrat/lib directory with more files in a subdirectory whose name will depend on your architecture.)

To do: Mention that the data generation scripts use the dscr package; see here.

If this does not work first time - e.g. because you don't have some dependencies installed - then install the dependencies and try again. This time on entering R you will have to tell packrat to try again yourself by typing packrat::restore(). If this still does not work for you, or you already have the packages you need installed then you may prefer to remove the packrat subdirectory and install the packages you need yourself. Quit R.

4. Within the repository directory type make clean. This will remove figure etc files that I have already included in the repository.

5. Within the repository directory type make. This will try to:

   i) Run all the code for the simulation studies. It will take a while (hours), so you might want to run it overnight. This should create a bunch of output files in the output directory. Particularly you will know that it worked iff you can find the files dsc-shrink-files/res.RData and dsc-robust-files/dsc_robust.RData.

   ii) Build/render the .Rmd files in the analysis directory. If successful you should have a file analysis/index.html that you can open to see a list of all the rendered files.

   iii) pdflatex the paper.

If you have problems (more than likely!) you might like to try each of these steps in turn, by sequentially typing make output, make analysis, and make paper.

If things go wrong

• If you have trouble installing Rmosek, maybe this will help.

• Ultimately you don't need Rmosek to make things run - if you don't have Rmosek installed then the ashr package will use an EM algorithm instead. The results from this method are very similar to those from using the interior point method (but the interior point method is faster and provides better convergence).

• If things go wrong in making the output files, try looking at the .Rout files created in the appropriate output subdirectory (output/dsc-shrink/, output/dsc-znull or output/dsc-robust) to see what went wrong.

• If things go wrong in making the analysis files, try looking at the .html files produced to see what went wrong.

Directory Structure

The directory structure here, and features of the analysis subdirectory (including the Makefile), are based on https://github.com/jdblischak/singleCellSeq. Here's a brief summary of the directory structure.

• analysis: Rmd files for investigations; will generate figures in figure/ subdirectory

• R: R scripts/functions used in analysis; no code that is actually run put here

• output: largish files of data, typically created by code (e.g. post-processed data, simulations)

• code: code used for preprocessing, generation of output files etc ; may be long-running

• data: datasets that generally will not change once deposited

• paper: the paper

• packrat: a directory that contains information about all the R package used. See the R package packrat for more details.

• talks: any presentations