The goal of mamut2r is to imports data coming from .xml files generated with the Fiji MaMuT plugin for lineage and tracking of biological objects. {mamut2r} also allows to create lineage plots.

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MeVisLab XMarkerShortestPath module (Dijsktra shortest path)

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AssayScope is an intuitive application dedicated to large scale image processing and data analysis. It is meant for histology, cell culture (2D, 3D, 2D+t) and phenotypic analysis. 

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TTK the Topology Toolkit


The Topology ToolKit (TTK) is an open-source library and software collection for topological data analysis in scientific visualization.

TTK can handle scalar data defined either on regular grids or triangulations, either in 2D or in 3D. It provides a substantial collection of generic, efficient and robust implementations of key algorithms in topological data analysis. It includes:
 · For scalar data: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, topological simplification;
 · For bivariate scalar data: fibers, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces;
 · For uncertain scalar data: mandatory critical points;
 · For time-varying scalar data: critical point tracking;
 · For high-dimensional / point cloud data: dimension reduction;
 · and more!


TTK makes topological data analysis accessible to end users thanks to easy-to-use plugins for the visualization front end ParaView. Thanks to ParaView, TTK supports a variety of input data formats.

TTK is written in C++ but comes with a variety of bindings (VTK/C++, Python) and standalone command-line programs. It is modular and easy to extend. We have specifically developed it such that you can easily write your own data analysis tools as TTK modules.

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NeuroMorph is a toolset designed to import, analyze, and visualize mesh models in Blender. It has been developed specifically for the morphological analysis of 3D objects derived from serial electron microscopy images of brain tissue, but much of its functionality can be applied to any 3D mesh. These mesh objects can be generated by any 3D image segmentation software, such as ilastik or Fiji

scikit-learn (sklearn)


Scikit-learn (sklearn) is a python library used for machine learning. sklearn contains simple and efficient tools for data mining and data analysis. Modules and functions include those for classification, regression, clustering, dimensionality reduction, model selection and data preprocessing. Many people have contributed to sklearn (list of authors)

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FoCuS-point is stand-alone software for TCSPC correlation and analysis. FoCuS-point utilizes advanced time-correlated single-photon counting (TCSPC) correlation algorithms along with time-gated filtering and innovative data visualization. The software has been designed to be highly user-friendly and is tailored to handle batches of data with tools designed to process files in bulk. FoCuS-point also includes advanced diffusion curve fitting algorithms which allow the parameters of the correlation functions and thus the kinetics of diffusion to be established quickly and efficiently.



FoCuS-scan is software for processing and analysis of large-scale scanning fluorescence correlation spectroscopy (FCS) data. FoCuS-scan can correlate data acquired on conventional turn-key confocal systems and in the form of xt image carpets.

Fit a model for the growth of yeast cells


This notebook uses the rOMERO-gateway and EBImage to process an Image associated to the paper 'Timing of gene expression in a cell-fate decision system'.

The Image "Pos22" is taken from the dataset idr0040-aymoz-singlecell/experimentA/YDA306_AGA1y_PRM1r_Mating. It is a timelapse Image with 42 timepoints separated by 5 minutes. This Image is used to fit a model for the growth of the yeast cells. The notebook does not replicate any of the analysis of the above mentioned paper.

Its purpose is mainly to demonstrate the use of Jupyter, rOMERO-gateway and EBimage.


What it does:

  • For each time point of one movie:
    • Read the image for this time point  from the IDR
    • Threshold the images and count the cells using EBimage functions
  • Fit an exponential model to the count of cells against time to get a coefficient of grow (exponential factor)




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Cell or particle Counting and scoring stained objects using CellProfiler


This is a Jupyter notebook demonstrating the run of a code from IDR data sets by loading a CellProfiler Pipeline 

The example here is applied on real data set, but does not correspond to a biological question. It aims to demonstrate how to create a jupyter notebook to process online plates hosted in the IDR.

It reads the plate images from the IDR.

It loads the CellProfiler Pipeline and replace the reading modules used to read local files from this defaults pipeline by module allowing to read data remotely accessible.

It creates a CSV file and displays it in the notebook.

It makes some plot with Matplotlib.