Statistics

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

Correlate

• 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

Visualise

• 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

Analyse

• 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

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 networks, machine learning, image processing, geometry, data science, visualizations, 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.

CLIJ2 is a GPU-accelerated image processing library for ImageJ/Fiji, Icy, Matlab and Java. It comes with hundreds of operations for filtering, binarizing, labeling, measuring in images, projections, transformations and mathematical operations for images. While most of these are classical image processing operations, CLIJ2 also allows performing operations on matrices potentially representing neighborhood relationships between cells and pixels.

CLIJ2 was developed to process images from fluorescence microscopy data of developing cells, tissues, organoids and organisms.

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. 

"The Microscope Image Analysis Toolbox MiToBo is an extension for the widely used image processing application ImageJ and its new release ImageJ 2.0.
MiToBo ships with a set of operators ready to be used as plugins in ImageJ. They focus on the analysis of biomedical images acquired by various types of microscopes."

NanoJ-SQUIRREL (Super-resolution Quantitative Image Rating and Reporting of Error Locations) is a software package designed for assessing and mapping errors and artefacts within super-resolution images. This is achieved through quantitative comparison with a reference image of the same structure (typically a widefield, TIRF or confocal image). SQUIRREL produces quantitative maps of image quality and resolution as well as global image quality metrics.

shinyHTM is an open source, web-based tool for data exploration, image visualization and normalization of High Throughput Microscopy data. Within shinyHTM the user is guided through a linear workflow which follows the following best practices:

• Inspect the numerical data through plotting
• Measurements are linked to raw images
• Perform quality control to exclude images with aberrations or where image analysis failed
• Perform a reproducible data analysis
• Normalize data and report statistical significance

Image visualization relies on Fiji/ImageJ, along with its wealth of analytical tools.

shinyHTM can be used to analyze image features obtained with CellProfiler, ImageJ or any other bioimage analysis software. The output of analysis is a publication-ready scoring of the data.

shinyHTM is based on the R shiny package.

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.

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.

Mean square displacement (MSD) analysis is a technique commonly used in colloidal studies and biophysics to determine what is the mode of displacement of particles followed over time. In particular, it can help determine whether the particle is:

• freely diffusing;
• transported;
• bound and limited in its movement.

On top of this, it can also derive an estimate of the parameters of the movement, such as the diffusion coefficient.

@msdanalyzer is a MATLAB per-value class that helps performing this kind of analysis. The user provides several trajectories he measured, and the class can derive meaningful quantities for the determination of the movement modality, assuming that all particles follow the same movement model and sample the same environment.

Python is a programming language.

Python 2.7.0 was released on July 3rd, 2010.

Python 2.7 is scheduled to be the last major version in the 2.x series before it moves into an extended maintenance period. This release contains many of the features that were first released in Python 3.1.

 A bugfix release, 2.7.16, is currently available. Its use is recommended.

Python is a programming language.

SciPy is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. With SciPy, an interactive Python session becomes a data-processing and system-prototyping environment. Find more about SciPy here!

Classification of trajectoire: need tracking results as input and will then classify the trajectories as  brownian motion, confined brownian or directed.

A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O.

The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction; EPI unwarping), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear model).

This R package implements the NBLAST neuron similarity algorithm described in a preprint available at http://dx.doi.org/10.1101/006346. In addition to basic pairwise comparison, the package implements search of databases of neurons. There is also suport for all x all comparison for a group of neurons. This can produce a distance matrix suitable for hierarchical clustering, which is also implemented in the package.

An R package for the (3D) visualisation and analysis of biological image data, especially tracings of single neurons. nat is the core package of a wider suite of neuroanatomy tools introduced at http://jefferislab.github.io.

Shiny is an R package that makes it easy to build interactive web apps straight from R.

Workflow of data-analysis strategies for image-based cell profiling.

1. Image analysis (image correction, image segmentation, feature extraction)
2. Image quality control
3. Preprocessing extracted features
4. Dimensionality reduction
5. Single-cell data aggregation
6. Measuring profile similarity
7. Assay quality assessment
8. Downstream analysis

Examples of publications where the workflow has been used at least partly:

• Image-based multivariate profiling of drug responses from single cells
• Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes
• Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping
• Cell shape and the microenvironment regulate nuclear translocation of NF‐κB in breast epithelial and tumor cells
• Genome-wide RNAi Screening Identifies Protein Modules Required for 40S Subunit Synthesis in Human Cells
• Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes
• Systematic morphological profiling of human gene and allele function via Cell Painting

Vaa3D is a handy, fast, and versatile 3D/4D/5D Image Visualization and Analysis System for Bioimages and Surface Objects. It also provides many unique functions that you may not find in other software. It is Open Source, and supports a very simple and powerful plugin interface and thus can be extended and enhanced easily.

Vaa3D is cross-platform (Mac, Linux, and Windows). This software suite is powerful for visualizing large- or massive-scale (giga-voxels and even tera-voxels) 3D image stacks and various surface data. Vaa3D is also a container of powerful modules for 3D image analysis (cell segmentation, neuron tracing, brain registration, annotation, quantitative measurement and statistics, etc) and data management. This makes Vaa3D suitable for various bioimage informatics applications, and a nice platform to develop new 3D image analysis algorithms for high-throughput processing. In short, Vaa3D streamlines the workflow of visualization-assisted analysis.

Vaa3D can render 5D (spatial-temporal) data directly in 3D volume-rendering mode; it supports convenient and interactive local and global 3D views at different scales... it comes with a number of plugins and toolboxes. Importantly, you can now write your own plugins to take advantage of the Vaa3D platform, possibly within minutes!

PopulationProfiler – is light-weight cross-platform open-source tool for data analysis in image-based screening experiments. The main idea is to reduce per-cell measurements to per-well distributions, each represented by a histogram. These can be optionally further reduced to sub-type counts based on gating (setting bin ranges) of known control distributions and local adjustments to histogram shape. Such analysis is necessary in a wide variety of applications, e.g. DNA damage assessment using foci intensity distributions, assessment of cell type specific markers, and cell cycle analysis.

Auto-Bayes is a software package based on Bayesian statistics and requires no user dependent parameters for molecule detection and image reconstruction for Single-Molecule Localization Microscopy (SMLM), including photoactivated localization microscope (PALM), stochastic optical reconstruction microscope (STORM), and direct stochastic optical reconstruction microscopy (dSTORM), etc.

This is an ImageJ plugin to analyze bacterial cells. It provides a user-friendly interface and a powerful suite of detection, analysis and data presentation tools. It works with individual phase or fluorescence images as well as stacks, hyperstacks, and folders of any of these types. Even large image sets are analyzed rapidly generating raw tabular data that can either be saved or copied as is, or have additional statistical analysis performed and graphically represented directly from within MicrobeJ, making it an all-in-one image analysis solution.

Evaluates the orientation of fiber orientation pattern and plots the results in the image. It calculates gradient in x and y direction. - then calculates the eigenvector of nematic tensor, which is the orientation of the pattern.

## About TANGO software is an open-source software for Analysis of Nuclear Genome Organization. It is composed of an ImageJ plugin for batch processing and analysis, and a R package for statistical analysis. Reference: 2528 ## Some key features - Image import uses bioimage formats. - Construction of workflow in GUI by choosing filters / segmentation strategy for - Prefiltering - Segmentation - Postfiltering - Isolated nuclei could individually be inspected, deleted from list and subjected for detailed analysis. - Uses MCIB3D library as backend. - Basic usage is to segment nucleus, crop them to single nucleus objects, segment substructures within objects and measure their properties. - Optionally R can be connected to do detailed analysis of results. - Uses MongoDB to manage huge data set.

For each ROI, provides the ratio of pixels over a given threshold over the total number of pixels in the ROI.

The workflow contains a Matlab package (plusTipTracker) for segmentation and tracking of microtubule tips, based on fluorescence time-lapse movies from microtubule tip markers such as EB-GFP. The tracking model accounts for the specific movement characteristics of microtubules Moreover, scripts for secondary analysis of detected microtubule paths are provided.

plusTipTracker is part of u-track 2.0 package. The workflow is described in the reference. 

Plots an intensity profile of a given ROI.

Can plot mean over T or/and Z too.

CellProfiler FlagImage module allows to assign a flag if an image meets certain measurement criteria that you specify (for example, if the image fails a quality control measurement). The value of the flag is 1 if the image meets the selected criteria (for example, if it fails QC), and 0 if it does not meet the criteria (if it passes QC).

Dedicated to score tracking dataset from the ISBI tracking challenge

The plugin analyses a point pattern (positions of objects of interest) distributed within a reference structure.

This analysis allows in particular to assess deviation from spatial randomness, and to reveal trends for clustering (attraction) or regularity (repulsion). No edge correction is performed, as it is assumed that no point is expected outside the reference structure.

This plugin comes together with 3D ImageJ suites plugin.

For each ROI, computes the number of pixel over a threshold. This plugin also provides the density and outputs results as an excel file.