Predicting the Reprogrammability of Human Cells Based on Transcriptome Data and SGD Classifier with Elastic-Net Regularization

Cell reprogramming has shown considerable importance in recent years; however, the programmability of cells and efficiency of reprogramming varies across different cell types. Considering several weeks of cell programming process and costly programming agents used through the process, every failure in reprogramming comes with a significant burden. Better planning for reprogramming experiments could be possible if there is a way of predicting the outcome of reprogramming before the experiments using transcriptome data. In this study, we have accessed the transcriptome data of successful or unsuccessful programming studies published in literature and constructed a Stochastic Gradient Descent (SGD) classifier with Elastic-Net regularization for predicting whether the cell lines are reprogrammable. We tested our classifier using 10-fold cross validation over cell lines and on each cell separately. Our results showed that it is possible to predict the outcome of cell reprogramming with accuracies up to 98% and Area Under the Curve (AUC) scores up to 0.98%. Considering the success of our experimental outcomes we conclude that an outcome of a cell reprogramming experiment can be predicted with high accuracy using machine learning on transcriptome data.