MultiCapsNet: a interpretable deep learning classifier integrate data from multiple sources

Abstract

Recent advances in experimental biology have generated huge amount of data. Due to differences present in detection targets and detection mechanisms, the produced data comes with different formats and lengths. There is an urgent call for computational methods to integrate these diverse data. Deep learning model is an ideal tool to cope with complex datasets, but its inherent black box nature needs more interpretability. Here, we present MultiCapsNet, a deep learning model built on CapsNet and scCapsNet. The MultiCapsNet model possesses the merits of both easier data integration and higher model interpretability. In the first example, we use the labeled variant call dataset, which is originally used to test the models for automating somatic variant refinement. We divide the 71 features listed in the dataset into eight groups according to data source and data property. Then, the data from those eight groups with different formats and lengths are integrated by our MultiCapsNet to predict the labels associated with each variant call. The performance of our MultiCapsNet matches the previous deep learning model well, given much less parameters than those needed by the previous model. After training, the MultiCapsNet model provides importance scores for each data source directly, while the previous deep learning model needs an extra importance determination step to do so. Despite that our MultiCapsNet model is substantially different from the previous deep learning model and the source importance measuring methods are also different, the importance score correlation between these two models is very high. In the second example, the prior knowledge, including information for protein-protein interactions and protein-DNA interactions, is used to determine the structure of MultiCapsNet model. The single cell RNA sequence data are decoupled into multiple parts according to the structure of MultiCapsNet model that has been integrated with prior knowledge, with each part represents genes influenced by a transcription factor or involved in a protein-protein interaction network and then could be viewed as a data source. The MultiCapsNet model could classify cells with high accuracy as well as reveal the contribution of each data source for cell type recognition. The high ranked contributors are often relevant to the contributed cell type.