Implementation in FastCMH algorithm in C and R. The R package fastcmh is on CRAN. The paper can be found here.

Either the R package can be used, or the C code can be used directly.

The easiest way to install the R package is via CRAN, by using the R console:

install.packages("Rcpp")    #dependency  
install.packages("bindata") #dependency  
install.packages("fastcmh")

or simply:

install.packages("fastcmh", dependencies=TRUE)

Suppose you have a data set with

• n samples (e.g. patients),
• L features (e.g. SNPs) that have a binary encoding,
• each sample has two possible labels (e.g. case/control),
• each sample one of K classes for the categorical covariate on which you wish to condition (e.g. country: Spain, France, Germany...).

In order to run FastCMH, three files are needed:

1. data.txt: a file containing L rows and n columns, each (i, j )th space-separated entry either 0 or 1 corresponding to the value of the binary feature for the i th feature of the j th sample.
2. label.txt: a file containing n rows, each row containing a single entry that is either 0 or 1, where the j th row gives the label for the j th sample.
3. cov.txt: a file containing K rows, each row containing a single positive integer, where the m th row indicates the number of samples that have the m th value of the categorical covariate.

Note: The rows in data.txt and label.txt need to be ordered so that the first n_1 rows have covariate class 1, the next n_2 rows have covariate class 2, ..., the final n_K rows have covariate class n_K.

Note: The files need not have the default file names above, but for the rest of this description we shall assume that this is the case.

This example shows a minimal synthetic dataset in order to illustrate the format of the data. It can be generated in R using the package fastcmh using the following commands:

library(fastcmh)
makefastcmhdata(folder="./", L=20, n=50, K=2, tau1=5, taulength1=4, tau2=12, taulength2=4, seednum=3)

This will create three files data.txt, label.txt, cov.txt in the current working directory. As the code suggests, this dataset has L=20 features, n=50 samples and K=2 classes for the categorical covariate. The true significant interval starts at tau1=5 and has length 4 (i.e. the interval [5,8]), while a confounded interval is created starting at tau2=12, and also with length 4 (i.e. the interval [12, 15]) The random seed is set to be 3.

Note: While the FastCMH algorithm will handle any number K for the number of classes of the categorical covariate, the R script makefastcmhdata will only generate synthetic data for K=2.

The contents of data.txt are (L=20 features/rows, n=50 samples/columns):

0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0
0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 1 1 1 1 0 0 0 1
0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 1
1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0
0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1
0 0 1 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0
0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0
1 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 1 1 1 0 1 1 0
0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0
1 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 1 0
0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 1 0 0 1 0 0

The label.txt file has n=50 rows (only first ten are shown):

0
0
0
1
1
1
1
1
1
0
[etc, truncated]

The cov.txt file has K=2 rows (each row with one entry, and the rows sum up to n=50):

22
28

The FastCMH algorithm can be run in R using the following code:

df <- runfastcmh(folder="./")

And the (filtered) significant intervals can be obtained simply using:

df$sig

which will return the following data frame:

  start end       pvalue
  1     5   8 0.0003525223

This concludes the description of the data format.

Note: The C version of the code uses a python script to filter the final results. Python and the numpy module need to be installed.

To compile the C version of the code, enter the C folder and run make:

cd C
make

In the C folder is a shell script which shows how to run the C code on the sample data:

data="../../sampledata/data.txt"
label="../../sampledata/label.txt"
cov="../../sampledata/cov.txt"

alpha=0.05
L_max=0

outputfolder="./output/"

basefile="fastcmh"
basefilename=$outputfolder$basefile

postprocessing_folder="../postprocessing/"

pval="allpval.txt"
pval_file=$outputfolder$pval

mkdir -p $outputfolder

./significant_interval_search_meta_cmh $data $label $cov $alpha $L_max $basefilename -postprocessing_folder $postprocessing_folder -pval_file $pval_file

Any questions can be directed to:

• Felipe Llinares Lopez (C code): felipe.llinares [at] bsse.ethz.ch
• Dean Bodenham (R package): dean.bodenham [at] bsse.ethz.ch