Figure 1

Flowcharts for evaluating the cross-platform transferability of signature genes and predictive models. Two analysis procedures were applied to evaluate the transferability of signature genes (a) and predictive models (b). In (a), microarray training data are used to develop 500 trained models through (c) to predict the microarray validation samples. The signature genes of each model are then used with the RNA-Seq training data to build an untrained RNA-Seq model using through (d) to predict the RNA-Seq validation samples. The performance of microarray models is finally compared to that of RNA-Seq models. The transferability of signature genes from RNA-Seq back to microarray data can conversely be calculated. While in (b), both microarray and RNA-Seq data were z-scored prior to model development. Then microarray training data are used to develop 500 trained models to predict both microarray and RNA-Seq validation samples. The performance of models in predicting microarray data is compared to that in predicting RNA-Seq data. From RNA-Seq back to microarray is conversely examined. A trained model is developed through (c). Briefly, training samples are randomly split in a 70/30 ratio. For each split, a series of models are developed using the 70% of training samples to predict the remaining 30%. The models are developed as follows: (1) all genes are first filtered with t-test P <0.05 and then ranked by fold change (FC); (2) a sequential forward feature selection by a step of two and parameter selection strategy is then used to build a number of models to predict the remaining samples. Finally, the signature genes and parameters of the best model are used with all training samples to build a trained model. An untrained model is built using all training samples from one platform but with the signature genes and parameters of a model trained from the other platform (d).