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HistoJS is a groundbreaking tool designed to transform spatial biology research. Are you facing challenges with highly-multiplexed immunofluorescence images? HistoJS is here to revolutionize your experience. With its open-source and extensible capabilities, HistoJS offers unparalleled insights into single-cell spatial relationships. Its interactive interface, powered by advanced machine learning algorithms, is tailored for the biomedical community, making complex analyses more accessible than ever. Manage, store, and analyze multi-channel OME-Tiff files with ease, thanks to Digital Slide Archive integration. Analyze your images remotely or locally, all within a user-friendly environment. Thanks for joining the HistoJS community today. Explore its vast potential and advance your research to new heights. Visit our Setup Environment page to get started!
HistoJS is a Web-based tool for Multiplexed images. The software can be deployed on clusters, as well as on the cloud or a local client-side machine.
*HistoJS Overview
| HistoJS Hack of JavaScript, CSS, HTML, Python Flask, Docker, OpenSeaDragon, Digital Slide Archive, WebGL, TFJS. |  |
| HistoJS can add connections to Digital Slide Archive (DSA) online servers and browse multiplex tissue images in ease and authentication mode. |  |
| Each OME-Tiff file represents a large biological multi-channel microscopy image of tissue samples. Each channel of the image represents the intensity of a specific marker (e.g., protein) distribution found in the biological cells. |  |
| Channel settings can be selected and adjusted (e.g., coloring, contrasting) for the best visualization experience. |  |
| Multiple blending and channel composite options are available with HistoJS to show marker distributions. |  |
| The HistoJS analysis phase of the cell populations can be performed using the tool analysis UI. The image shows how to perform cell gating, phenotyping, cell interactions, and many other options. |  |
OME-TIFF Dataset Sources: nanoString and Rashid et al.
Biological Statistical tasks such as biological cell biomarkers histogram, sample statistics quartiles, cell classification, correlations, spatial analysis, and quantification of specific marker expression are available for cell analysis and discovering the cell interactions.
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