MOTIVATION: Detection of gene regulatory aberrations enhances our ability to interpret the impact of inherited and acquired genetic variation for rare disease diagnostics and tumor characterization. While numerous methods for calling RNA expression outliers from RNA-sequencing data have been proposed, the establishment of protein expression outliers from mass spectrometry data is lacking. RESULTS: Here, we propose and assess various modeling approaches to call protein expression outliers across three datasets from rare disease diagnostics and oncology. We use as independent evidence the enrichment for outlier calls in matched RNA-seq samples and the enrichment for rare variants likely disrupting protein expression. We show that controlling for hidden confounders and technical covariates, while simultaneously modeling the occurrence of missing values, is largely beneficial and can be achieved using conditional autoencoders. Moreover, we find that the differences between experimental and fitted log-transformed intensities by such models exhibit heavy tails that are poorly captured with the Gaussian distribution and report stronger statistical calibration when instead using the Student's t-distribution. Our resulting method, PROTRIDER, outperformed baseline approaches based on raw log-intensities Z-scores, PCA, and isolation-based anomaly detection with Isolation forests. The application of PROTRIDER reveals significant enrichments of AlphaMissense pathogenic variants in protein expression outliers. Overall, PROTRIDER provides a method to confidently identify aberrantly expressed proteins applicable to rare disease diagnostics and cancer proteomics. AVAILABILITY AND IMPLEMENTATION: PROTRIDER is freely available at github.com/gagneurlab/PROTRIDER and also available on Zenodo under the DOI zenodo.15569781.

In this work we present a method to intuitively issue control over devices in smart environments, to display data that smart objects and sensors provide, and to create and manipulate flows of information in smart environments. This makes it easy to customize smart environments by linking arbitrary data sources to various display modalities on the fly. Touchscreen smartphones - as readily available multi-purpose devices - are used to overlay real objects with virtual controls. We evaluated this system with a first qualitative user study.

We present an Augmented Reality (AR) system to support collaborative tasks in a shared real-world interaction space by facilitating joint attention. The users are assisted by information about their interaction partner's field of view both visually and acoustically. In our study, the audiovisual improvements are compared with an AR system without these support mechanisms in terms of the participants' reaction times and error rates. The participants performed a simple object-choice task we call the "gaze game" to ensure controlled experimental conditions. Additionally, we asked the subjects to fill in a questionnaire to gain subjective feedback from them. We were able to show an improvement for both dependent variables as well as positive feedback for the visual augmentation in the questionnaire.