I want to implement a model that predicts disease from RNA-seq data. Therefore, the training data were normalized by limma voom, and trained by randomForest.
However, new input data has not been normalized in the same way as training data. If I have to normalized the new data with training data, I need to create a new prediction model with new normalized data.
Please, let me know how to normalize for new data in the same way with training data for fixed model prediction.
RNAseq_train # Training data RNAseq_test # Test data (but can not be combined with RNAseq_train samples) DesignMatrix <- model.matrix(~0 + Design) keep=rowSums(cpm(RNAseq_train)>=1)>=keep_n RNAseq_train_filtered=RNAseq_train[keep,] DGE=DGEList(RNAseq_train_filtered) DGE=calcNormFactors(DGE,method =c("TMM")) RNAseq_train_normalized=voom(DGE,DesignMatrix, plot=F)$E
but how 'RNAseq_test' normalized in the same way with RNAseq_train_normalized? (In the situation that RNAseq_test was new cohort data after training model)