I am working on a differential expression analysis, and would like to explore the genes that are the most informative for the contrast I'm studying. I have sorted the genes by magnitude of fold change (1/fold change if < 0) and looked at the top 10, but highest fold change does not necessarily mean most diagnostic between the 2 conditions. I'm reading an older paper that used leave-one-out cross-validation class prediction to identify the most predictive genes in a differential expression analysis. However, the software they cite requires an expensive paid license, and no doubt has changed significantly in 10 years.
What software is available for leave-one-out cross-validation class prediction these days? Can you provide a simple example usage? My preference would be an R-based solution, but I'm flexible on that point.
Not sure about the out-of-the-box solution, but if I were you, I would try to go with some basic machine learning method, such as LASSO, Elastic Net (glmnet) or Random Forest (randomForest), and extract the most important features for classification using a fold-based validation:
CVTools in R, partition your data (training and test)If you think the predictions look reasonable, you can extract the features easily from your fit and use them for new classifications.
I agree, and also the R randomForest packages has built-in feature importance estimation which is based on some sort of cross-validation. You might also want to try a random GLM (http://www.biomedcentral.com/1471-2105/14/5) as a promising mix of GLMs and RFs.
I ended up using the Weka data mining software. I wrote this script to convert the data to ARFF format, and then I did feature selection using information gain. As I have 11 samples, I set it to do 11-fold cross-validation, which is leave-one-out cross-validation. To confirm, I extracted only the top 25 features (genes) and loaded only these into Weka and got near-perfect classification using several classification methods.
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