CODEWITHSUNDEEP
rmachine-learningbioinformaticscategorical-datafeature-selection
Svdv
Svdv

Reputation: 51

Using mRMRe for feature selection : my categorical target variable is sometimes selected

I have a data frame "data" with 60 rows (=samples) and 20228 columns where the first column is my target variable (an ordered factor : 0 or 1) and the other columns are my features (=numeric). I want to do a feature selection with mRMRe in a loop corresponding to a 5-cross-validation that I do 3 times. I select every time 25 features. Here is the problematic part of my code : 

library(caret)
library(mRMRe)

data <- read.csv("home/RNA_seq.csv", row.names=1, sep=";", stringsAsFactors=FALSE)
data <- data.frame(t(data))
data[,1] <- factor(data[,1])
data[,1] <- ordered(data[,1], levels = c("0", "1"))

features_select <- list()

r <- 5 # 5-cross-validation
t <- 3 # 5-cross-validation done 3 times
  for (j in 1:t){
    for (i in 1:r){
      #5-cross-validation
      train.index <- createFolds(factor(data$Response), k = 5, list = TRUE, returnTrain = TRUE) 
      datatrain <- data[train.index[[i]],]
      datatest  <- data[-train.index[[i]],]

      #Feature selection
      data.mrmre.train <- mRMR.data(data=datatrain)
      res.fs.mrmr <- mRMR.classic(data=data.mrmre.train, target_indices=1, feature_count=25)
      selected.features.mrmre <- mRMRe::solutions(res.fs.mrmr)
      features_select[[((j-1)*r+i)]] <- res.fs.mrmr@feature_names[unlist(selected.features.mrmre)]
      print(features_select[[((j-1)*r+i)]])
      print(res.fs.mrmr)
    }
  }

My problem is that sometimes my target variable called "Response"(=column 1 of "data") is selected by mRMRe. By example : 

features_select :

[[1]]
[1] "AC137800.2" "AC007387.1" "AC079354.1" "AC145138.1" "RNA5SP370" 
[6] "RNA5SP219"  "AL022324.1" "AC023449.1" "AP000873.1" "AC020612.2"
[11] "RNA5SP473"  "AC092810.1" "IGKV1D.37"  "SST"        "AC093331.1"
[16] "TRAJ34"     "AC107983.1" "RPL39P"     "HSBP1P1"    "TRBJ1.6"   
[21] "PHGR1"      "RNA5SP435"  "RNA5SP301"  "AC005255.1" "KRT127P"

[[2]]
 [1] "AC073869.8"   "Response" "Response" "Response" "Response" "Response"
 [7] "Response" "Response" "Response" "Response" "Response" "Response"
[13] "Response" "Response" "Response" "Response" "Response" "Response"
[19] "Response" "Response" "Response" "Response" "Response" "Response"
[25] "Response"

Here is the output of the function mRMR.classic() in the first case and in the second case (=bad case) :

[[1]]
Formal class 'mRMRe.Filter' [package "mRMRe"] with 8 slots
  ..@ filters       :List of 1
  .. ..$ 1: int [1:25, 1] 18837 18781 15503 15526 17437 20028 18924 17133 17024 16104 ...
  ..@ scores        :List of 1
  .. ..$ 1: num [1:25, 1] 0.817 0.819 0.817 0.817 0.817 ...
  ..@ mi_matrix     : num [1:20228, 1:20228] NA -0.3786 -0.1536 -0.0929 -0.0964 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:20228] "Response" "TMSB15B" "MATR3" "HSPA14" ...
  .. .. ..$ : chr [1:20228] "Response" "TMSB15B" "MATR3" "HSPA14" ...
  ..@ causality_list:List of 1
  .. ..$ 1: num [1:20228] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
  ..@ sample_names  : chr [1:48] "Pt1_28" "Pt2_28" "Pt4_28" "Pt5_28" ...
  ..@ feature_names : chr [1:20228] "Response" "TMSB15B" "MATR3" "HSPA14" ...
  ..@ target_indices: int 1
  ..@ levels        : int [1:25] 1 1 1 1 1 1 1 1 1 1 ...

[[2]]
Formal class 'mRMRe.Filter' [package "mRMRe"] with 8 slots
  ..@ filters       :List of 1
  .. ..$ 1: int [1:25, 1] 1 1 1 1 1 1 1 1 1 1 ...
  ..@ scores        :List of 1
  .. ..$ 1: num [1:25, 1] 0 0 0 0 0 0 0 0 0 0 ...
  ..@ mi_matrix     : num [1:20228, 1:20228] NA -0.518 -0.246 -0.211 -0.204 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:20228] "Response" "TMSB15B" "MATR3" "HSPA14" ...
  .. .. ..$ : chr [1:20228] "Response" "TMSB15B" "MATR3" "HSPA14" ...
  ..@ causality_list:List of 1
  .. ..$ 1: num [1:20228] NA NaN NaN NaN NaN NaN NaN NaN NaN NaN ...
  ..@ sample_names  : chr [1:48] "Pt1_28" "Pt2_28" "Pt4_28" "Pt5_28" ...
  ..@ feature_names : chr [1:20228] "Response" "TMSB15B" "MATR3" "HSPA14" ...
  ..@ target_indices: int 1
  ..@ levels        : int [1:25] 1 1 1 1 1 1 1 1 1 1 ...

This doesn't appear every time for the same value of i and j into the loop. Do you have an idea where is the problem ?

Thank you in advance !

Upvotes: 3

Views: 893

Answers (1)

Svdv
Svdv

Reputation: 51

I got a response from the authors of the mRMRe package. The solution is to use the "strata" parameter to indicate my target variable (= ordered factor) in the mRMR.data() function. So, I had to change:

data.mrmre.train <- mRMR.data(data=datatrain)

to:

data.mrmre.train <- mRMR.data(data=datatrain[,-1], strata=datatrain[,1]).

For more details, see: https://github.com/bhklab/mRMRe/issues/27

Upvotes: 2

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