Find open-source science resources

Tapenade is a quantitative pipeline for analyzing multi-layered organoids, enabling deep imaging and quantification of cell-scale properties across tissue scales. It provides a user-friendly Python package and napari plugins for 3D nuclei segmentation and gene expression analysis.

An open-source multiplexed image t-SNE viewer.

spatialproteomics is a Python package for analyzing highly multiplexed immunofluorescence imaging data, offering end-to-end coverage of the workflow with flexibility and automation. It processes images larger than working memory and supports synchronization of shared coordinates across data modalities.

An easy-to-use deep learning pipeline for image segmentation and classification. InstandDL: An easy and convenient deep learning pipeline for image segmentation and classification. InstantDL enables experts and non-experts to use state-of-the art deep learning methods on biomedical image data. InstantDL offers the four most common tasks in medical image processing: Semantic segmentation, instance segmentation, pixel-wise regression and classification. For more in depth discussion on the methods, as well as comparing the results and bechmarks using this package, please refer to our preprint on bioRxiv here.

ILEE is an anguided algorithm for accurate and automated quantitative analysis of cytoskeletal images.

Cheetah is a simple to use Python library to support the development of analysis workflows and real-time cybergenetic control platforms that combine microscopy imaging and environmental control. The toolkit relies on a U-Net convolutional neural network to perform cell segmentation and uses this with additional functions that can label, track and generate control signals based on this information.

Charisma is an R package that performs reproducible color characterization of digital images, classifying colors into discrete categories using a biologically-inspired approach. It provides a standardized framework suitable for large-scale studies of biological organisms.

A general framework for interpreting wide-band neural activity. DeepInsight is a toolbox for the analysis and interpretation of wide-band neural activity and can be applied on unsorted neural data. This means the traditional step of spike-sorting can be omitted and the raw data can be used directly as input, providing a more objective way of measuring decoding performance.

CellCountCV – a web-application for accurate cell counting and automated batch processing of microscopy images using fully-convolutional neural networks. This repository contains the code and examples accompaining our preprint https://doi.org/10.1101/867218. Working_with_CellCounter.ipynb — this notebook contains the examples of pretrained FCNN model usage in Python and through JSON-RPC calls to CellCountCV

An automated approach to spectral unmixing of fluorescent biological images.

YeastMate is a tool for the automated detection and segmentation of S. cerevisae cells and their mating and budding events, as well as a subclassification of the cells involved in these events into mother and daughter cells.

The bardensr (barcode dexmixing through non-negative spatial regression) package is a collection of tools for modeling linearly multiplexed observations. There is some true signal we are interested in, but our observations are some noisy linear mixture of those signals.

Profiling biological perturbations with high-content imaging in single cells and heterogeneous populations. Multidimensional profiling to quantify complex changes via the minimum covariance determinant.

A Platform for Structural Variant Visual Validation and Automated Filtering. samplot is a command line tool for rapid, multi-sample structural variant visualization. samplot takes SV coordinates and bam files and produces high-quality images that highlight any alignment and depth signals that substantiate the SV. 1000 genomes high coverage crams.