Metadata-Version: 2.1
Name: Mistic
Version: 0.0.1
Summary: Mistic: A package for rendering multiple multiplexed images simultaneously
Home-page: https://github.com/MathOnco/Mistic
Author: Sandhya Prabhakaran
Author-email: Sandhya.Prabhakaran@moffitt.org
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/MathOnco/Mistic/issues
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: PhenoGraph
Requires-Dist: scikit-image
Requires-Dist: seaborn
Requires-Dist: bokeh
Requires-Dist: tifffile
Requires-Dist: Pillow

Mistic: image tSNE visualizer
=============================

This is a Python tool using the Bokeh library to view multiple multiplex
images simultaneously. The code has been tested on 7-panel Vectra TIFF,
32- & 64-panel CODEX TIFF, 16-panel CODEX QPTIFF and 44-panel t-CyCIF
TIFF images.

Mistic’s GUI with user inputs is shown below:

![](https://github.com/MathOnco/Mistic/raw/main/fig_readme/Figure_2.jpg)


**Figure description:** A sample Mistic GUI with user inputs is shown.
**A.** User-input panel where imaging technique choice, stack montage
option or markers can be selected, images borders can be added, new or
pre-defined image display coordinates can be chosen, and a theme for the
canvases can be selected. **B.** Static canvas showing the image t-SNE
colored and arranged as per user inputs. **C.** Live canvas showing the
corresponding t-SNE scatter plot where each image is represented as a
dot. The live canvas has tabs for displaying additional information per
image. Metadata for each image can be obtained by hovering over each
dot.

Features of Mistic
------------------

-  Two canvases:

   -  still canvas with the image tSNE rendering
   -  live canvases with tSNE scatter plots for image metadata rendering

-  Dropdown option to select the imaging technique: Vectra, t-CyCIF, or
   CODEX
-  Option to choose between Stack montage view or multiple multiplexed
   images by selecting the markers to be visualised at once
-  Option to place a border around each image based on image metadata
-  Option to use a pre-defined tSNE or generate a new set of tSNE
   co-ordinates
-  Option to shuffle images with the tSNE co-ordinates
-  Option to render multiple tSNE scatter plots based on image metadata
-  Hover functionality available on the tSNE scatter plot to get more
   information of each image
-  Save, zoom, etc each of the Bokeh canvases

Requirements
------------

-  Python >= 3.6 

   -  Install Python from here: https://www.python.org/downloads/



Additional information
----------------------

-  For instructions on how to run Mistic on the t-CyCIF data, please
   check:
   https://mistic-rtd.readthedocs.io/en/latest/vignette_example_tcycif.html

-  For instructions on how to run Mistic on the toy data from our NSCLC
   Vectra FoVs, please
   check:https://mistic-rtd.readthedocs.io/en/latest/vignette_example_vectra.html

-  Paper on bioRxiv:
   https://www.biorxiv.org/content/10.1101/2021.10.08.463728v1

-  Documentation: https://mistic-rtd.readthedocs.io

-  Code has been published at Zenodo:
   https://doi.org/10.5281/zenodo.5912169 

-  Toy data is published here: https://doi.org/10.5281/zenodo.6131933

-  Mistic is highlighted on Bokeh’s user showcase: http://bokeh.org/