NEAT performance on datasets using limited tumour samples per patient

Code written by Alex Lubbock code@alexlubbock.com in Ian Overton's group ian.overton@ed.ac.uk. Licensed under CC-BY-NC-SA v4 (please see LICENSE.txt also available at: http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)

Introduction

This code loads protein expression data from reverse-phase protein arrays along with patient age, and examines the effect of including differing numbers of tumour samples per patient on validation of the 'NEAT' algorithm (N-cadherin, EPCAM, Age, mTOR).

The sampling regime uses Sobol sampling, a quasi-random technique which ensures low discrepancy, together with a mixed radix indexing system which allows us to apply the sampling consistently even where patients have differing numbers of samples.

One master combinadic (combination identifier) is generated for each sample combination. For a given dataset, this is then mapped to a set of combinadics, one for each patient being sampled. Finally, this identifier is mapped onto the set of samples to include in the dataset under consideration.

The approach has the advantages of sampling the available range of samples evenly (Sobol sequences are quasi-random, low discrepancy), deterministically and with traceability (from the master combinadic and the raw data, we can easily determine which patients and which samples were included). The deterministic nature can also support parallel execution with guaranteed non-overlapping sample sets.

Requirements

This code has been tested on R 3.2.2 using Linux and Mac OSX, but is expected to work on Windows and other recent versions of R.

On our test machine (2014 Macbook Pro with Core i7 "Haswell" 2.5GHz), memory usage (RAM) stayed under 200Mb. The output files require around 150Mb of free disk space. Execution time was approximately 9 hours.

Instructions

The following R packages need to be installed:

• randtoolbox
• gmp
• plyr
• survival

These can be installed from within R with the following command:

install.packages(c("randtoolbox", "gmp", "plyr", "survival"))

The command below runs the analysis:

Rscript dosampling.R

Three files will be output: 1-sample-HR.tsv, 2-sample-HR.tsv and 3-sample-HR.tsv. These files contain NEAT model predictive results using 1, 2 or 3 tumour samples per patient respectively.

Each file contains a three column table with one million rows. Each row is the NEAT model predictive result for a given combinadic

• a particular combination of tumour samples from the validation cohort. The three columns are:

1. Combinadic ID (a unique identifier for the combinadic)
2. Log hazard ratio for NEAT on the samples in that combinadic
3. Log-rank p-value

To override the default of one million samples per number of tumour samples, open the dosampling.R script and change the NUM.SAMP.COMBN variable near the top. Please note that the NUM.SAMP.COMBN value must be an integer multiple of 1000.

If the script is re-run, output files will be overwritten without warning.

List of Files

NEAT-model.RData - An RData object containing the NEAT model itself ("coxph" class object from R's "survival" library) combinadics.R - R functions for combinadic and mixed radix arithmetic dosampling.R - R script for performing the tumour sampling procedure described above expressiondata.csv - Anonymised per-tumour sample RPPA expression data for the three NEAT protein markers (N-Cadherin, EPCAM and mTOR) patientdata.csv - Anonymised ages, survival time and survival status README.md - This README file LICENSE.txt - The CC-BY-NC-SA license text