Relate is a correlative software package optimised to work with EM, EDS, EBSD, & AFM data and images.  It provides the tools you need to correlate data from different microscopes, visualise multi-layered data in 2D and 3D, and conduct correlative analyses.

  • Combining data from different imaging modalities (e.g. AFM, EDS & EBSD)

  • Interactive display of multi-layer correlated data

  • Analytical tools for metadata interrogation

  • Documented workflows and processes


  • Import data from AZtec using the H5oina file format
  • Import AFM data
  • Correlate both sets of data using intuitive image overlays and image matching tools
  • Produce combined multimodal datasets


  • 2D display of multi-layered data
  • 3D visualisation of topography combined with AFM material properties, EM images, and EDS & EBSD map overlays
  • Customisation of colour palettes, data overlays, image rendering options, and document display
  • Export images and animations


  • Generate profile (cross section) views of multimodal data
  • Measure and quantify data across multiple layers
  • Analyse areas via data thresholding using amount of x-ray counts, phase maps, height, or other material properties.
  • Select an extensive range of measurement parameters
  • Export analytical data to text or CSV files
Relate analysis workflow example

Phindr3D is a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) high content screening image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and data visualization.

Please see our GitHub page and the original publication for details.


KNIME workflow to visualize a dataset described by multiple quantitative features (ex: a list of samples or cells, each described with multiple morphological features) as a 3D cloud of points (each point corresponding to one sample/cell) as well as a line plot (1 line per sample/cell).

For the 3D plot, the workflow uses Principal Component Analysis (PCA) for dimensionality reduction, ie it simplifies the information for each sample from n-features to 3 pseudo-features which are used as x,y,z-coordinates for each sample. The original features should cover similar value range, to make sure the PCA is not biased towards the large values features. One option is to normalize the values (min/max or Z-score). 

Also make sure that the resulting PCA represents a decent % of the original data variance (at least 70%). Otherwise the PCA plot will not be representative of the original data-distribution. The % is shown in the title of the PCA plot.

The workflow is interactive and so selecting in one panel of the figure will highlight in the other panel too.

It was originally published for the visualization of phenotypic kidney features in zebrafish, but the workflow is generic by design and can be reused for any quantitative feature set. 


Wolfram Mathematica (usually termed Mathematica) is a modern technical computing system spanning most areas of technical computing — including neural networksmachine learningimage processinggeometrydata sciencevisualizations, and others. The system is used in many technical, scientific, engineering, mathematical, and computing fields.


CellProfiler Analyst (CPA) allows interactive exploration and analysis of data, particularly from high-throughput, image-based experiments. Included is a supervised machine learning system which can be trained to recognize complicated and subtle phenotypes, for automatic scoring of millions of cells. CPA provides tools for exploring and analyzing multidimensional data, particularly data from high-throughput, image-based experiments analyzed by its companion image analysis software, CellProfiler.