Publications by authors named "Achille Nazaret"
Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference.
View Article and Find Full Text PDFBiological insights often depend on comparing conditions such as disease and health, yet we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics.
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- Heart rate responses during workouts indicate overall fitness and cardiovascular health, but existing models are often limited to controlled lab settings and don’t account for data from wearables.
- A new hybrid approach combines a physiological model with neural networks to create personalized fitness representations by analyzing heart rate dynamics in relation to various external factors like terrain and weather.
- This model, tested on data from over 270,000 workouts, accurately predicts heart rate responses with a low median error and shows strong correlation with traditional fitness metrics, making it interpretable and useful for health applications.
Nudges are interventions promoting healthy behavior without forbidding options or substantial incentives; the Apple Watch, for example, encourages users to stand by delivering a notification if they have been sitting for the first 50 minutes of an hour. On the basis of 76 billion minutes of observational standing data from 160,000 subjects in the public Apple Heart and Movement Study, we estimate the causal effect of this notification using a regression discontinuity design for time series data with time-varying treatment. We show that the nudge increases the probability of standing by up to 43.
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