Publications by Year: 2024

MOTIVATION: Nonlinear low-dimensional embeddings allow humans to visualize high-dimensional data, as is often seen in bioinformatics, where datasets may have tens of thousands of dimensions. However, relating the axes of a nonlinear embedding to the original dimensions is a nontrivial problem. In particular, humans may identify patterns or interesting subsections in the embedding, but cannot easily identify what those patterns correspond to in the original data.

RESULTS: Thus, we present SlowMoMan (SLOW Motions on MANifolds), a web application which allows the user to draw a one-dimensional path onto a 2D embedding. Then, by back-projecting the manifold to the original, high-dimensional space, we sort the original features such that those most discriminative along the manifold are ranked highly. We show a number of pertinent use cases for our tool, including trajectory inference, spatial transcriptomics, and automatic cell classification.

AVAILABILITY AND IMPLEMENTATION: Software: https://yunwilliamyu.github.io/SlowMoMan/; Code: https://github.com/yunwilliamyu/SlowMoMan.

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

BACKGROUND:  While substantial anatomical study has been pursued throughout the human body, anatomical study of the human lymphatic system remains in its infancy. For microsurgeons specializing in lymphatic surgery, a better command of lymphatic anatomy is needed to further our ability to offer surgical interventions with precision. In an effort to facilitate the dissemination and advancement of human lymphatic anatomy knowledge, our teams worked together to create a map. The aim of this paper is to present our experience in mapping the anatomy of the human lymphatic system.

METHODS:  Three steps were followed to develop a modern map of the human lymphatic system: (1) identifying our source material, which was "Anatomy of the human lymphatic system," published by Rouvière and Tobias (1938), (2) choosing a modern platform, the Miro Mind Map software, to integrate the source material, and (3) transitioning our modern platform into The Human BioMolecular Atlas Program (HuBMAP).

RESULTS:  The map of lymphatic anatomy based on the Rouvière textbook contained over 900 data points. Specifically, the map contained 404 channels, pathways, or trunks and 309 lymph node groups. Additionally, lymphatic drainage from 165 distinct anatomical regions were identified and integrated into the map. The map is being integrated into HuBMAP by creating a standard data format called an Anatomical Structures, Cell Types, plus Biomarkers table for the lymphatic vasculature, which is currently in the process of construction.

CONCLUSION:  Through a collaborative effort, we have developed a unified and centralized source for lymphatic anatomy knowledge available to the entire scientific community. We believe this resource will ultimately advance our knowledge of human lymphatic anatomy while simultaneously highlighting gaps for future research. Advancements in lymphatic anatomy knowledge will be critical for lymphatic surgeons to further refine surgical indications and operative approaches.

OBJECTIVES: To demonstrate that 2 popular cohort discovery tools, Leaf and the Shared Health Research Information Network (SHRINE), are readily interoperable. Specifically, we adapted Leaf to interoperate and function as a node in a federated data network that uses SHRINE and dynamically generate queries for heterogeneous data models.

MATERIALS AND METHODS: SHRINE queries are designed to run on the Informatics for Integrating Biology & the Bedside (i2b2) data model. We created functionality in Leaf to interoperate with a SHRINE data network and dynamically translate SHRINE queries to other data models. We randomly selected 500 past queries from the SHRINE-based national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) network for evaluation, and an additional 100 queries to refine and debug Leaf's translation functionality. We created a script for Leaf to convert the terms in the SHRINE queries into equivalent structured query language (SQL) concepts, which were then executed on 2 other data models.

RESULTS AND DISCUSSION: 91.1% of the generated queries for non-i2b2 models returned counts within 5% (or ±5 patients for counts under 100) of i2b2, with 91.3% recall. Of the 8.9% of queries that exceeded the 5% margin, 77 of 89 (86.5%) were due to errors introduced by the Python script or the extract-transform-load process, which are easily fixed in a production deployment. The remaining errors were due to Leaf's translation function, which was later fixed.

CONCLUSION: Our results support that cohort discovery applications such as Leaf and SHRINE can interoperate in federated data networks with heterogeneous data models.