The HuBMAP Computational Tools Component is responsible for development of standardized processing pipeline for all HuBMAP data, which is the foundation that enables joint storage, indexing, display, querying and mapping of HuBMAP datasets. A key innovative aspect that we have championed and, which as far as we know is unique for large consortia with several groups, platforms and data modalities is the fact that all HuBMAP pipelines we implemented provide uniform processing of all data from the same modality. Specifically, for each of these modalities we use the same computational algorithm for initial and downstream processing. For example, all scRNA-Seq datasets, regardless of which of the 7 platforms they were generated by, are processed by our Salmon quantification pipeline. This makes integration and comparison much easier across all HuBMAP data. In addition, all downstream analysis including dimensionality reduction, clustering, differential expression etc. are also performed using the same methods for all platforms making it easy to organize the display of the data on the portal and to index the data for quantitative queries. Similar pipelines have been developed for other modalities. In the production phase of the HuBMAP Consortium, we will extend these computational pipelines to support additional data types from new tissue mapping centers and other data providers. We will additionally extend our focus beyond single-cell image segmentation to support the mapping efforts of the Human Reference Atlas (HRA) collaboration, producing hierarchical links from cells to functional tissue units to higher-level anatomical structures.

Public Health Relevance Statement: Uniform processing of many disparate data types reduces friction in integrating disparate data between modalities, tissues, and data providers, allowing clinicians and public health experts to find biological and clinically-relevant insights in large biomolecular, spatial, and imaging datasets. We will expand on our previous work to link the results of large-scale HuBMAP data processing to the Human Reference Atlas (HRA) efforts, ensuring that HuBMAP datasets are highly represented in the HRA.