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===R package oposSOM analysis=== * Wirth, Loeffler, v.Bergen, Binder: Expression cartography of human tissues using self organizing maps. (BMC Bioinformatics 2011) library(oposSOM) A = read.table("geno_GA_30.methylMatrix_min27_std0.1", header=TRUE, row.name=1) A$STDEV=NULL A$min_RD=NULL A$mean_RD=NULL B = as.matrix(A) run.pipeline(B, dataset.name="AMD_std0.1_30", dim.som1=20, dim.som2=20) *Understanding the dim.som1 and dim.som2 **dim.som1 = size of the primary SOM / resolution of the expression profiles: number of tiles equals dim.som1^2. **dim.som2 = resolution of the second level SOM: number of tiles equals dim.som2^2 *Since the analysis doesn't allow for missing values, can we iteratively switch out samples to find a converging best minimal set? **To allow for ~4% missing values, I can switch out 2 samples in control and in GA at each iteration. **The number of ways 2 samples could be selected out of 48: 48!/(48-2)! = 2256. Since, there are control and GA, = 2256^2 = 5,089,536 **Do 1 million random permutation to sample 20% of the solution space? But since we only allow for 4% missing values, there should be very little variations between the solutions. But with probe capture, we only get around 80-90% sites shared between two samples. **Lets try permuting two solutions each, from 35+35, 37+37, 39+39, 41+41, 43+43, 45+45, 47+47 to see how much overlaps are there between two sets.
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