The CellOrganizer project provides tools for
- learning generative models of cell organization directly from images
- storing and retrieving those models
- synthesizing cell images (or other representations) from one or more models
Model learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies.
CellOrganizer can learn models of
- cell shape
- nuclear shape
- chromatin texture
- vesicular organelle size, shape and position
- microtubule distribution.
These models can be conditional upon each other. For example, for a given synthesized cell instance, organelle position is dependent upon the cell and nuclear shape of that instance.
Cell types for which generative models for at least some organelles have been built include human HeLa cells, mouse NIH 3T3 cells, and Arabidopsis protoplasts. Planned projects include mouse T lymphocytes and rat PC12 cells.
- TBA
Installing Docker is beyond the scope of this document. To learn about Docker Community Edition (CE), click here.
To build an image using the Dockerfile in this repository, run the command
➜ bash ./build.sh
The previous step should build an image
➜ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
icaoberg/cellorganizer-jupyter latest be76be002cbd 36 hours ago 9.25GB```
To run a container using the image above
If you do not wish to build the container and would like to pull the latest copy from Dockerhub, running
➜ bash ./pull.sh
To spin a container from the image above, running
bash ./run.sh
Running the script run.sh
- create a folder on your Desktop called
data - download a collection of images to
data - will start the container
When contributing to this repository, please first discuss the change you wish to make via issue, email, or any other method with the owners of this repository before making a change.
Support for CellOrganizer has been provided by grants GM075205, GM090033 and GM103712 from the National Institute of General Medical Sciences, grants MCB1121919 and MCB1121793 from the U.S. National Science Foundation, by a Forschungspreis from the Alexander von Humboldt Foundation, and by the Freiburg Institute for Advanced Studies.
Copyright (c) 2007-2019 by the Murphy Lab at the Computational Biology Department in Carnegie Mellon University