Convert galgo.Obj to data.frame

Source: R/convert-functions.R

The current function transforms a galgo.Obj to a data.frame

to_dataframe(output)

Arguments

output

An object of class galgo.Obj

Value

The current function restructurates a galgo.Obj to a more easy to understand an use data.frame. The output data.frame has \( m x n\) dimensions, were the rownames (\(m\)) are the solutions obtained by the galgo algorithm. The columns has the following structure:

  1. Genes: The features included in each solution in form of a list

  2. k: The number of partitions found in that solution

  3. SC.Fit: The average silhouette coefficient of the partitions found

  4. Surv.Fit: The survival fitness value

  5. Rank: The solution rank

  6. CrowD: The solution crowding distance related to the rest of the solutions

Examples

# load example dataset
library(breastCancerTRANSBIG)
data(transbig)
Train <- transbig
rm(transbig)
#> Warning: object 'transbig' not found

expression <- Biobase::exprs(Train)
clinical <- Biobase::pData(Train)
OS <- survival::Surv(time = clinical$t.rfs, event = clinical$e.rfs)

# We will use a reduced dataset for the example
expression <- expression[sample(1:nrow(expression), 100), ]

# Now we scale the expression matrix
expression <- t(scale(t(expression)))

# Run galgo
output <- GSgalgoR::galgo(generations = 5, population = 15,
prob_matrix = expression, OS = OS)
#> Using CPU for computing pearson distance
#> Generation 1 Non-dominated solutions:
#>          k                      rnkIndex CrowD
#> result.6 2 0.13475022  75.17647        1   Inf
#> result.8 6 0.08097386 221.10851        1   Inf
#> Generation 2 Non-dominated solutions:
#>          k                      rnkIndex    CrowD
#> result.6 2 0.13475022  75.17647        1      Inf
#> result.8 6 0.08097386 221.10851        1      Inf
#> result.6 2 0.13067741 115.20417        1 1.118927
#> Generation 3 Non-dominated solutions:
#>          k                      rnkIndex     CrowD
#> result.8 6 0.08097386 221.10851        1       Inf
#>          2 0.15772653  35.86713        1       Inf
#> result.6 2 0.13067741 115.20417        1 1.0597222
#> result.6 2 0.13475022  75.17647        1 0.5630868
#> Generation 4 Non-dominated solutions:
#>          k                      rnkIndex     CrowD
#>          2 0.15772653  35.86713        1       Inf
#>          7 0.02010858 231.07312        1       Inf
#> result.8 6 0.08097386 221.10851        1 1.1840778
#> result.6 2 0.13067741 115.20417        1 0.8233753
#>          2 0.13569288 111.35387        1 0.5206216
#> Generation 5 Non-dominated solutions:
#>          k                      rnkIndex     CrowD
#>          2 0.15772653  35.86713        1       Inf
#>          7 0.01589945 255.30887        1       Inf
#> result.8 6 0.08097386 221.10851        1 1.1466026
#> result.6 2 0.13067741 115.20417        1 0.7878776
#>          7 0.02010858 231.07312        1 0.5318530
#>          2 0.13569288 111.35387        1 0.4949618
outputDF <- to_dataframe(output)
outputList <- to_list(output)