FOntCell: Fusion of Ontologies of Cells - Cabau-Laporta, Javier; Ascensión, Alex M.; Arrospide-Elgarresta, Mikel; Gerovska, Daniela; Araúzo-Bravo, Marcos J. (Frontiers in Cell and Developmental Biology, 2021)
High-throughput cell-data technologies, such as single-cell RNA-seq, create a demand for algorithms for automatic cell classification and characterization. There exist several cell classification ontologies with complementary information. However, one needs to merge them to synergistically combine their information. The main difficulty in merging is to match the ontologies since they use different naming conventions. Therefore, we developed an algorithm that merges ontologies by integrating the name matching between class label names with the structure mapping between the ontology elements based on graph convolution. Since the structure mapping is a time consuming process, we designed two methods to perform the graph convolution: vectorial structure matching and constraint-based structure matching. To perform the vectorial structure matching, we designed a general method to calculate the similarities between vectors of different lengths for different metrics. Additionally, we adapted the slower Blondel method to work for structure matching. To implement our algorithms, we developed FOntCell, a software module in Python for efficient automatic parallel-computed merging, fusion, of ontologies in the same or similar knowledge domains. FOntCell allows the unification of dispersed knowledge in one domain into a unique ontology, producing the results of the merged ontology in OBO format, which can be iteratively reused by FOntCell to continuously adapt the ontologies with new data, such of the Human Cell Atlas, endlessly produced by data-driven classification methods. To navigate easily across the merged ontologies, it generates HTML files with tabulated and graphic summaries, and an interactive circular Directed Acyclic Graphs of the merged results. We used FOntCell to merge CELDA, LifeMap and LungMAP Human Anatomy cell ontologies to produce comprehensive cell ontology. We compared FOntCell with ontology alignment tools for the alignment of mouse and human anatomy ontologies task proposed by the Ontology Alignment Evaluation Initiative (OAEI) and found that the F alignment accuracies of FOntCell are over the mean of the other tools, and more importantly it outperforms significantly the best OAEI tools in the cell ontology alignment in terms of F alignment accuracies.