COSINE (version 2.0)

set1_GA: Result of genetic algorithm search for simulated data set1

Description

This data set contains the result of the subnetwork extraction using genetic algorithm applied to the analysis of the differential expression pattern between simulated dataset1 and the control dataset

Usage

data(set1_GA)

format

The format is: List of 4 $ Subnet_size: num [1:5] 23 58 61 75 99 $ Best_Scores: num [1:5] 16.7 24.7 26.6 29.3 77.7 $ Subnet :List of 5 ..$ : int [1:23] 36 40 41 112 121 148 163 184 185 206 ... ..$ : int [1:58] 25 36 38 39 40 41 61 71 78 79 ... ..$ : int [1:61] 25 36 38 39 40 41 48 61 78 79 ... ..$ : int [1:75] 8 25 36 38 39 40 41 61 71 78 ... ..$ : int [1:99] 3 5 8 11 25 29 36 38 39 40 ... $ GA_obj :List of 5 ..$ :List of 11 .. ..$ type : chr "binary chromosome" .. ..$ size : int 500 .. ..$ popSize : num 200 .. ..$ iters : num 1000 .. ..$ suggestions : NULL .. ..$ population : num [1:200, 1:500] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ elitism : num 40 .. ..$ mutationChance: num 0.05 .. ..$ evaluations : num [1:200] -16.7 -16.7 -16.7 -16.7 -16.7 ... .. ..$ best : num [1:1000] -2.85 -2.85 -3.68 -4.49 -4.59 ... .. ..$ mean : num [1:1000] 0.0192 -0.743 -1.4033 -2.1104 -2.7789 ... .. ..- attr(*, "class")= chr "rbga" ..$ :List of 11 .. ..$ type : chr "binary chromosome" .. ..$ size : int 500 .. ..$ popSize : num 200 .. ..$ iters : num 1000 .. ..$ suggestions : NULL .. ..$ population : num [1:200, 1:500] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ elitism : num 40 .. ..$ mutationChance: num 0.05 .. ..$ evaluations : num [1:200] -24.7 -24.7 -24.7 -24.7 -24.7 ... .. ..$ best : num [1:1000] -4.27 -5.88 -6.64 -6.73 -7.95 ... .. ..$ mean : num [1:1000] 0.00387 -1.27523 -2.59219 -3.73991 -4.7593 ... .. ..- attr(*, "class")= chr "rbga" ..$ :List of 11 .. ..$ type : chr "binary chromosome" .. ..$ size : int 500 .. ..$ popSize : num 200 .. ..$ iters : num 1000 .. ..$ suggestions : NULL .. ..$ population : num [1:200, 1:500] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ elitism : num 40 .. ..$ mutationChance: num 0.05 .. ..$ evaluations : num [1:200] -26.6 -26.6 -26.6 -26.6 -26.6 ... .. ..$ best : num [1:1000] -5.13 -6.63 -6.84 -7.4 -8.47 ... .. ..$ mean : num [1:1000] 0.0412 -1.3408 -2.6099 -3.9691 -5.1945 ... .. ..- attr(*, "class")= chr "rbga" ..$ :List of 11 .. ..$ type : chr "binary chromosome" .. ..$ size : int 500 .. ..$ popSize : num 200 .. ..$ iters : num 1000 .. ..$ suggestions : NULL .. ..$ population : num [1:200, 1:500] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ elitism : num 40 .. ..$ mutationChance: num 0.05 .. ..$ evaluations : num [1:200] -29.3 -29.2 -29.2 -29.2 -29.2 ... .. ..$ best : num [1:1000] -4.43 -4.71 -6.21 -6.92 -8.31 ... .. ..$ mean : num [1:1000] -0.126 -1.546 -2.71 -3.8 -4.788 ... .. ..- attr(*, "class")= chr "rbga" ..$ :List of 11 .. ..$ type : chr "binary chromosome" .. ..$ size : int 500 .. ..$ popSize : num 200 .. ..$ iters : num 1000 .. ..$ suggestions : NULL .. ..$ population : num [1:200, 1:500] 0 0 0 0 0 0 0 0 0 0 ... .. ..$ elitism : num 40 .. ..$ mutationChance: num 0.05 .. ..$ evaluations : num [1:200] -77.7 -77.6 -77.3 -77.3 -77.3 ... .. ..$ best : num [1:1000] -11.7 -15.2 -15.2 -20.6 -21.4 ... .. ..$ mean : num [1:1000] -0.0381 -2.8258 -5.5397 -8.3003 -10.7343 ... .. ..- attr(*, "class")= chr "rbga"

Details

This dataset is a list containing the following components: Subnet_size: A vector of length 5 showing the size of the selected subnetwork using five different lambdas. Best_Scores: The scores of the selected subnetworks corresponding to five lambdas. Subnet: The selected subnetworks (gene indices) for five lambdas. GA_obj: The objected returned by the function "rbga.bin", which stores the results of the genetic algorithm.

Examples

data(set1_GA)