Hi, I have RNA seq data containing over 10K genes and I would like to find the optimal feature fitter the classification model. I am wondering how to find the optimal feature count. Here is my code ,
mrEnsemble <- mRMR.ensemble(data = Xdata, target_indices = c(1) ,feature_count = 100 ,solution_count = 1)
mrEnsemble_genes <- as.data.frame(apply(solutions(mrEnsemble)[[1]], 2, function(x, y) { return(y[x]) }, y=featureNames(Xdata)))
View(mrEnsemble_genes)
I just set feature_count = 100 but I am wondering how to find the optimal feature count for classification without setting the number.
and the result after extracting mrEnsemble_genes will be the list of genes like,
gene05
gene08
gene45
gene67
Are they ranked by score calculated from mutual information? I mean the first ranked gene gain the highest MI and it may be a good gene for classifying the class of sample i.e. cancer and normal, right ? Thank you
Hi, I have RNA seq data containing over 10K genes and I would like to find the optimal feature fitter the classification model. I am wondering how to find the optimal feature count. Here is my code ,
mrEnsemble <- mRMR.ensemble(data = Xdata, target_indices = c(1) ,feature_count = 100 ,solution_count = 1)
mrEnsemble_genes <- as.data.frame(apply(solutions(mrEnsemble)[[1]], 2, function(x, y) { return(y[x]) }, y=featureNames(Xdata)))
View(mrEnsemble_genes)
I just set feature_count = 100 but I am wondering how to find the optimal feature count for classification without setting the number.
and the result after extracting mrEnsemble_genes will be the list of genes like,
gene05
gene08
gene45
gene67
Are they ranked by score calculated from mutual information? I mean the first ranked gene gain the highest MI and it may be a good gene for classifying the class of sample i.e. cancer and normal, right ? Thank you