I am France ROSE (she/her),
post-doctoral researcher
at the University Hospital of Cologne.

Deep learning (DL) methods show great success in handling sequential data, including text (natural language processing) and human movement (action prediction). However, they are scarcely used to understand animal behavior. However, motion carries rich additional information about neuronal and motor functioning. Animal models, such as rodents, can be studied under different medical conditions and in different environments. With recent advances in cameras and computer vision, animal behaviour can be measured through video recordings and motion capture systems, producing high quality data of an individual’s motion. The dynamics of postures that generate behaviours are complex, spanning multiple spatio-temporal scales, and there is currently a lack of computational methods to tackle this complexity.

In this project, I develop new DL methods for quantitative description of animal movement, more particularly to better understand how normal behaviors are impaired under specific pharmacological treatments. They will lead to better understand how normal behaviors (e.g. walking, balance, orientation in space, very subtle motion changes) are impaired under specific pharmacological treatments. This project also aims at improving deep learning methods, by comparing, testing and combining different modularities of neural networks, focusing on unsupervised learning, and pushing the understanding of inferred representations. The resulting methods will be applicable to other domains and most importantly can pave the way to detailed quantitative study of human behaviour including its early changes preceding neurodegenerative deterioration of the brain. Decoding behaviour - finding out what it means and predicting it - bears a great potential for improved diagnostics and new therapeutic strategies for neural disorders.

Keywords: behavior, representation learning, recurrent neural networks (RNNs), graph convolutional neural networks (GCNs)

Machine learning (ML) has recently been demonstrated to rival expert-level human accuracy in prediction and detection tasks in a variety of domains, including medicine. Despite these impressive findings, a key barrier to the full realization of ML’s potential in medical prognoses is technology acceptance. Recent efforts to produce explainable AI (XAI) have made progress in improving the interpretability of some ML models, but these efforts suffer from limitations intrinsic to their design: they work best at identifying why a system fails, but do poorly at explaining when and why a model’s prediction is correct. We posit that the acceptability of ML predictions in expert domains is limited by two key factors: the machine’s horizon of prediction that extends beyond human capability, and the inability for machine predictions to incorporate human intuition into their models.

We propose the use of a novel ML architecture, Neural Ordinary Differential Equations (NODEs) to enhance human understanding and encourage acceptability. Our approach prioritizes human cognitive intuition at the center of the algorithm design, and offers a distribution of predictions rather than single outputs. We explain how this approach may significantly improve human-machine collaboration in prediction tasks in expert domains such as medical prognoses. We propose a model and demonstrate, by expanding a concrete example from the literature, how our model advances the vision of future hybrid human-AI systems.

Keywords: Neural Ordinary Differential Equations (NODEs), XAI, medical prognosis

Article: Fompeyrine, D.A., Vorm, E.S., Ricka, N., Rose. F, & Pellegrin, G.. (2021). Enhancing human-machine teaming for medical prognosis through neural ordinary differential equations (NODEs). Hum.-Intell. Syst. Integr., p. 1-15

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell–cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect.

The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. This toolbox allows to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. It can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. The available spatial tests are available for both categorical and continuous cell features.

Keywords: spatial analysis, single-cell, statistical test, python

Article: Rose, F., Rappez, L., Triana, S. H., Stadler, M., Heikewalder, M., Alexandrov, T., & Auguste Genovesio. (2020) PySpacell : A Python package for spatial analysis of cell images. Cytometry Part A

In phenotypic cell-based assays, drugs are directly tested onto cells to assess their effect. Using cells allow scanning a wide spectrum of possible drug targets at once. Cell-based assays proved to be efficient at discovering first-in-class therapeutic drugs. However, posterior identification of a drug's mechanism of action (MOA) has remained difficult and highly refractory to automated analyses. Methods increasing the number of fluorescent dyes to reveal relevant cellular components were suggested for MOA prediction. We demonstrated that adding fluorescent dyes to a single assay has limited impact on MOA prediction accuracy, as monitoring only the nuclei stain could reach compelling levels of accuracy. This observation suggested that multiplexed measurements are correlated and nuclei stain could possibly reflect the general state of the cell. We then hypothesized that combining unrelated and possibly simple cell-based assays could be used to predict a drug target. We trained an ensemble classifier to predict drug targets and prioritize a possibly large list of unknown compound hits at once. Moreover, we show that such a combination of past screen data is usally found in screening facilities and can be re-used without additional experimental costs.

Keywords: High-Content Screening (HCS), random forests, ensemble classifier

Article: Rose, F.*, Basu, S.*, Rexhepaj, E., Chauchereau, A., Del Nery , E. & Genovesio, A. (2017) Compound Functional Prediction Using Multiple Unrelated Morphological Profiling Assays. SLAS TECHNOLOGY:Translating Life Sciences Innovation.

Robotics and automated fluorescence microscopes have promoted high-content cell-based screenings: fluorescent probes targeting DNA or other cell components are used to image hundreds of thousands of cells under many different conditions. Cell-based assays have proven to be efficient at discovering first-in-class therapeutic drugs, i.e. drugs acting on a new target. They allow to detect promising molecules and to associate functional annotations to them, like their molecular target or mechanism of action (MOA).

Even clonal cells respond differently under the same treatment. I studied this heterogeneity and its impact on drug profiling. Clustering approaches can be used to uncover cell subpopulations. To evaluate the additional information brought by the subpopulation approach, I compared the performances of clustering on a MOA prediction. I used an open-source dataset where 38 drugs were tested on a breast cancer cell line and imaged with three fluorophores targeting DNA, Actin and Tubulin (BBBBC021 from the Broad Institute). I additionally tested the reproducibility of the clustering algorithms. For both performance and reproducibility, the PhenoGraph (or Louvain) algorithm was the best choice.

Additionally the modelization was enriched with the spatial organization of cell subpopulations. I found that neighboring cells influence each others, and display a similar phenotype more frequently than expected at random. These results assessed across a hundred of treatments, show that even genetically identical cells are not all alike and independent, but create spatial heterogeneity via cell lineage and interaction. Using spatial information as well as phenotypic heterogeneity with graph kernel methods improves the MOA classification under some conditions.

Keywords: High-Content Screening (HCS), mechanism of action(MOA), clustering, graph kernels

Article: See chapter 2 of my Ph.D. thesis: Analysis of phenotypic and spatial cellular heterogeneity from large scale microscopy data. (2019) - In French

Coated micropatterns on slides allow individual growing cell clusters at large scale. Combined with automated microscopy, tens of thousands of videos of these growing cell clusters could be recorded. These videos can help identifying factors involved in cell growth, cell division or tissue formation by testing multiples perturbations (genetic or drug). The focus of the video analysis was the division angle from 2 to 3 cells and which perturbations caused a change in the division angle distribution.

However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. The image analysis of those large dynamic datasets with no possible human intervention is particularly challenging and has proven impossible using out-of-the-box automated cell detection methods.

We proposed a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with 2 and 3 components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We used the time resolved analysis to identify with high precision the very first frame containing three cells. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters.

Keywords: Gaussian mixture, High throughput, Time-lapse microscopy, Cell detection

Article: Li, Y.*, Rose, F.*, di Pietro, F., Morin, X., & Genovesio, A. (2016). Detection and tracking of overlapping cell nuclei for large scale mitosis analyses. BMC Bioinformatics, 17(1).