I want to select top k genes from the gds data and then i want to apply some classification algorithm to find the how much one gene selection algorithms (t-test,chi sq test,mRMR etc) works better from each other.I have used following R code to generate expression set from gds data.
library(GEOquery)
gds4515=getGEO(filename="GDS4515.soft.gz")
eset=GDS2eSet(gds4515,do.log2=TRUE)
Now i don't know what should i do now. At first have i to normalize it or have to do something else. if i have to normalize it that how can i do it. And after that what should i do.
GDS records have been normalized by the submitter. If you agree that the normalization is appropriate, you could proceed with your analysis. You say "select top k genes" and then "apply some classification algorithm" and then "gene selection algorithms". I am not at all clear on what you are actually trying to do.
Dear Sean Davis,
I am working in a project in which i have to compare the performances of different gene selection algorithms (feature selection algorithms ) i.e t-test,chi square test,mRMR etc. I am working on two class genes microarray colon cancer data. At first i will divide the data into two parts 1. Training set and 2. Test Set and i will apply the above algo. in training set. Since a microarray contain very less number of samples and large number of genes(features). I want to reduce the no. of genes by different feature or gene selection algo. and have to compare the performances from each other. For comparing the performances i will use a classification algorithm i.e SVM to classify the test set.