Package: leaspy
Leaspy - LEArning Spatiotemporal Patterns in PythonLeaspy is a software package for the statistical analysis oflongitudinal data, particularlymedicaldata that comes in a form ofrepeated observationsof patients at different time-points.Get started LeaspyInstallationLeaspy requires Python >= 3.7, < 3.10Create a dedicated environment (optional):Usingcondaconda create --name leaspy python=3.7
conda activate leaspyOr usingpyenvpyenv virtualenv leaspy
pyenv local leaspyInstall leaspy:pip install leaspyIt will automatically install all needed dependencies.DocumentationAvailable online atReadthedocs.ioExamples & TutorialsTheexample/start/folder contains a starting point if you want to launch your first scripts and notebook with the Leaspy package.
You can find additional walkthroughs in:this series ofonline tutorialsfrom 2020thisMedium postof 2019 (warning: the plotter and the individual parameters described there have been deprecated since then)DescriptionLeaspy is a software package for the statistical analysis oflongitudinal data, particularlymedicaldata that comes in a form ofrepeated observationsof patients at different time-points.
Considering these series of short-term data, the software aims at :recombining them to reconstruct the long-term spatio-temporal trajectory of evolutionpositioning each patient observations relatively to the group-average timeline, in term of both temporal differences (time shift and acceleration factor) and spatial differences (different sequences of events, spatial pattern of progression, ...)quantifying impact of cofactors (gender, genetic mutation, environmental factors, ...) on the evolution of the signalimputing missing valuespredicting future observationssimulating virtual patients to un-bias the initial cohort or mimics its characteristicsThe software package can be used with scalar multivariate data whose progression can be modelled by a logistic shape, an exponential decay or a linear progression.
The simplest type of data handled by the software are scalar data: they correspond to one (univariate) or multiple (multivariate) measurement(s) per patient observation.
This includes, for instance, clinical scores, cognitive assessments, physiological measurements (e.g. blood markers, radioactive markers) but also imaging-derived data that are rescaled, for instance, between 0 and 1 to describe a logistic progression.Main featuresfit: determine thepopulation parametersthat describe the disease progression at the population levelpersonalize: determine theindividual parametersthat characterize the individual scenario of biomarker progressionestimate: evaluate the biomarker values of a patient at any age, either for missing value imputation or future predictionsimulate: generate synthetic data from the modelFurther informationMore detailed explanations about the models themselves and about the estimation procedure can be found in the following articles :Mathematical framework:A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations. Jean-Baptiste Schiratti, Stéphanie Allassonnière, Olivier Colliot, and Stanley Durrleman. The Journal of Machine Learning Research, 18:1–33, December 2017.Open Access.Application to imaging data:Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks. I. Koval, J.-B. Schiratti, A. Routier, M. Bacci, O. Colliot, S. Allassonnière and S. Durrleman. MICCAI, September 2017.Open AccessApplication to imaging data:Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns. Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, and Stanley Durrleman. Front Neurol. 2018 May 4;9:235.Open AccessApplication to data with missing values:Learning disease progression models with longitudinal data and missing values. R. Couronne, M. Vidailhet, JC. Corvol, S. Lehéricy, S. Durrleman. ISBI, April 2019.Open AccessIntensive application for Alzheimer's Disease progression:AD Course Map charts Alzheimer's disease progression, I. Koval, A. Bone, M. Louis, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos, B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonniere & S. Durrleman, Scientific Reports, 2021. 11(1):1-16Open Accesswww.digital-brain.org: website related to the application of the model for Alzheimer's diseaseDisease Course Mappingwebpage by Igor KovalLicenseThe package is distributed under the BSD 3-Clause license.SupportThe development of this software has been supported by the European Union H2020 program (project EuroPOND, grant number 666992, project HBP SGA1 grant number 720270), by the European Research Council (to Stanley Durrleman project LEASP, grant number 678304) and by the ICM Big Brain Theory Program (project DYNAMO).ContactARAMIS Lab