arXiv subjects

Andreas Bueckle

Publications and source records attributed to Andreas Bueckle.

3 recordsLinked to original sources

arXiv2026

Compiling, Validating, and Using Human Reference Atlas Cell Type Annotation Crosswalks

The Human Reference Atlas (HRA) v2.5 includes 4,807 anatomical structures, 1,347 cell types, and 2,215 biomarkers (genes, proteins, lipids); 81 organs with 1,392 anatomical structures modeled in three dimensions (3D). Mapping new data into the HRA requires 3D spatial registration and/or cell type annotation (CTann). Many CTann tools exist but few use cell type names linked to Cell Ontology (CL), making comparisons across tools, tissues, and organs difficult. The HRA supports five transcriptomics CTann tools (Azimuth, CellTypist, popV, FR-Match, Pan-Human Azimuth), four sc-proteomics CTann tools (DeepCell Types, DeepCell Types-HuBMAP, Robust Image-Based Cell Annotator [RIBCA], and SpaTial cELl LeARning [STELLAR]), plus a Cell Distance Explorer (CDE) Spatial Omics collection covering previously published annotations across 12 tissue types with 47,349,496 cells. Assigned cell types differ in number, resolution, and coverage. This paper details how 10 crosswalks were constructed, validated, and used to compile a "Supertree" that can be used to compare CTann across organs and technologies in support of reference atlas constitution and usage. All data and code can be accessed at https://cns-iu.github.io/hra-ctann-supporting-information.

cs.HC↗

arXiv2021

Optimizing Performance and Satisfaction in Matching and Movement Tasks in Virtual Reality with Interventions Using the Data Visualization Literacy Framework

Virtual reality (VR) has seen increased use for training and instruction. Designers can enable VR users to gain insights into their own performance by visualizing telemetry data from their actions in VR. Our ability to detect patterns and trends visually suggests the use of data visualization as a tool for users to identify strategies for improved performance. Typical tasks in VR training scenarios are manipulation of 3D objects (e.g., for learning how to maintain a jet engine) and navigation (e.g., to learn the geography of a building or landscape before traveling on-site). In this paper, we present the results of the RUI VR (84 subjects) and Luddy VR studies (68 subjects), where participants were divided into experiment and control cohorts. All subjects performed a series of tasks: 44 cube-matching tasks in RUI VR and 48 navigation tasks through a virtual building in Luddy VR (all divided into two sets). All Luddy VR subjects used VR gear; RUI VR subjects were divided across three setups: 2D Desktop (with laptop and mouse), VR Tabletop (in VR, sitting at a table), and VR Standup (in VR, standing). In an intervention called "Reflective phase," the experiment cohorts were presented with data visualizations, designed with the Data Visualization Literacy Framework (DVL-FW), of the data they generated during the first set of tasks before continuing to the second part of the study. For Luddy VR, we found that experiment users had significantly faster completion times in their second trial (p = 0.014) while scoring higher in a mid-questionnaire about the virtual building (p = 0.009). For RUI VR, we found no significant differences for completion time and accuracy between the two cohorts in the VR setups; however, 2D Desktop subjects in the experiment cohort had significantly higher rotation accuracy as well as satisfaction (p(rotation) = 0.031, p(satisfaction) = 0.040).

cs.HC↗

arXiv2021

3D Virtual Reality vs. 2D Desktop Registration User Interface Comparison

Working with organs and extracted tissue blocks is an essential task in surgery and anatomy environments. To prepare specimens from human donors for analysis, wet-bench workers must dissect human tissue and collect metadata for downstream analysis, including information about the spatial origin of tissue. The Registration User Interface (RUI) was developed to allow stakeholders in the Human Biomolecular Atlas Program (HuBMAP) to register tissue blocks, i.e., to record the size, position, and orientation of human tissue data with regard to reference organs. In this paper, we compare three setups for registering one 3D tissue block object to another 3D reference organ (target) object. The first setup is a 2D Desktop implementation featuring a traditional screen, mouse, and keyboard interface. The remaining setups are both virtual reality (VR) versions of the RUI: VR Tabletop, where users sit at a physical desk which is replicated in virtual space; VR Standup, where users stand upright while performing their tasks. We then ran a user study for these three setups involving 42 human subjects completing 14 increasingly difficult and then 30 identical tasks in sequence and reporting position accuracy, rotation accuracy, completion time, and satisfaction. While VR Tabletop and VR Standup users are about three times as fast and about a third more accurate in terms of rotation than 2D Desktop users (for the sequence of 30 identical tasks), there are no significant differences between the three setups for position accuracy when normalized by the height of the virtual kidney across setups.

cs.HC↗