Analyzes, processes and visualizes multi-dimensional microscopy images. BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes.
Employs colocalization across different microscopy platforms. frcoloc can recognize representative spectra for one cellular compartment in fact reads. It is based on a random forest classifier and tested with conventional k-fold cross validation and leave-one-out cross-validation methods. This tool can predict a crisp segmentation, which makes the result accessible to cross-validation.
Extracts quantitative and biologically relevant information from single molecule localization microscopy (SMLM) data sets. ClusDoC combines two analysis techniques of cluster detection and colocalization. The software provides outputs at the level of molecules (e.g., distribution of DoC scores), of clusters (e.g., size, density, morphology), and of cells (e.g., comparisons across different conditions). It can also generate maps for visualization of the data. ClusDoC can be applied to address a variety of biological questions in different fields.
Allows spatial point pattern and interaction analysis. MosaicIA provides standardized ways to (1) correct for the influence of the distribution of points within one set onto the distance distribution to another set; (2) infer parameters of the interaction potential, such as the strength and length scale of the interaction; (3) perform statistical hypothesis tests for the presence of an interaction. It has been tested on both synthetic and real-world data.
Provides a method for determining true and random colocalization for experimental scenarios. CDA calculates modified Manders coefficients, M1ROI and M2ROI and detects colocalization in cellular and subcellular compartments thanks to standard confocal microscopy and computational image correlation techniques. The module can be used through the ImageJ software and can be applied to various fields including fluorescent wide field or confocal microscopy.
Restores images from microscopic data. Huygens is based on the deconvolution approach that reassigns out-of-focus light to its origin, thus improves signal-to-noise in images. It can use physically-acquired or simulated point-spread functions (PSFs) for characterization of optical system being deconvolved. The tool shows high-performance in in-house tests on deconvolution compared to other software packages. It provides intuitive wizards for parameter selection and processing.
Serves for colocalization analysis. Coloc consists in a Fiji's plugin that implements and performs the pixel intensity correlation. This tool requires a two color image for realizing analysis, and users can specify their region of interest (ROI) for obtaining accurate results. It gathers an auto threshold calculation method and effect of noise on Pearson's and Manders' coefficients.
Performs the Costes test for statistical significance. Colocalization test is ImageJ plugin that executes one of a set of three statistical tests. It also compares the Persons correlation coefficient of two colour channels in the real image data against a white noise image, or the same image data with one of the colour channels spatially shifted.
Allows users to set the thresholds for colocalization analysis. Colocalization can generate a 2D Histogram/Scatterplot/Fluorogram useful for visualizing the correlation of the pixel intensities, over all pixels/voxels in the image. It also makes a linear regression fit of the data in the scatter plot. It does auto threshold determination, and uses an iterative procedure to determine what pair of thresholds for the 2 channels of the scatterplot give a Pearson's correlation coefficient (r) of zero for the pixels below the thresholds.