NAM Maize data preparation scripts
Matlab C++ Shell Objective-C
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00README.txt
NAM_Map_20090730.txt
README
binarySearch.m
constants.txt
fastphase_chr1.txt
fastphase_chr10.txt
fastphase_chr2.txt
fastphase_chr3.txt
fastphase_chr4.txt
fastphase_chr5.txt
fastphase_chr6.txt
fastphase_chr7.txt
fastphase_chr8.txt
fastphase_chr9.txt
imputedIBMMarkersGWAS.chr1.082809.txt
imputedIBMMarkersGWAS.chr10.082809.txt
imputedIBMMarkersGWAS.chr2.082809.txt
imputedIBMMarkersGWAS.chr3.082809.txt
imputedIBMMarkersGWAS.chr4.082809.txt
imputedIBMMarkersGWAS.chr5.082809.txt
imputedIBMMarkersGWAS.chr6.082809.txt
imputedIBMMarkersGWAS.chr7.082809.txt
imputedIBMMarkersGWAS.chr8.082809.txt
imputedIBMMarkersGWAS.chr9.082809.txt
imputedMarkersGWAS.chr1.082809.txt
imputedMarkersGWAS.chr10.082809.txt
imputedMarkersGWAS.chr2.082809.txt
imputedMarkersGWAS.chr3.082809.txt
imputedMarkersGWAS.chr4.082809.txt
imputedMarkersGWAS.chr5.082809.txt
imputedMarkersGWAS.chr6.082809.txt
imputedMarkersGWAS.chr7.082809.txt
imputedMarkersGWAS.chr8.082809.txt
imputedMarkersGWAS.chr9.082809.txt
makefile
prepare.sh
projectAll.m
projection.cpp
projection.m
residuals_distancebased_for_asi.txt
timings.txt

README

This is an example of SNP projection using NAM Maize data.

Run ./prepare.sh to prepare inputs for SNP projection.
This will remove column headers, combine imputed markers, and remove unused data columns for input to the Matlab projection script.

In Matlab, run projectAll to project SNPs for all chromosomes.
The resulting projected data files will be ~4 GB each.