https://github.com/luispedro/coelho2015_netsdetermination

Reproducible code archive for "Automatic Determination of NET (Neutrophil Extracellular Traps) Coverage in Fluorescent Microscopy Images" by Coelho et al.
https://github.com/luispedro/coelho2015_netsdetermination

Last synced: about 1 year ago
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Reproducible code archive for "Automatic Determination of NET (Neutrophil Extracellular Traps) Coverage in Fluorescent Microscopy Images" by Coelho et al.

• Host: GitHub
• URL: https://github.com/luispedro/coelho2015_netsdetermination
• Owner: luispedro
• Created: 2015-02-06T17:37:30.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2018-09-20T13:24:29.000Z (almost 8 years ago)
• Last Synced: 2025-04-01T01:51:58.593Z (about 1 year ago)
• Language: Python
• Homepage: http://doi.org/10.1093/bioinformatics/btv156
• Size: 24.1 MB
• Stars: 3
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.rst

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README

          
============================  

Determination of NET Content

============================  

This package contains support material (code & data) for the paper:

    *Automatic Determination of NET (Neutrophil Extracellular Traps) Coverage

    in Fluorescent Microscopy Images* by Luis Pedro Coelho, Catarina Pato, Ana

    Friães, Ariane Neumann, Maren von Köckritz-Blickwede, Mário Ramirez, and

    João André Carriço, Bioinformatics *2015 Jul 15;31(14):2364-70*

    `DOI:10.1093/bioinformatics/btv156

    `__.

Use in academic publications should cite the paper above.

Code is provided under the MIT license.

Data

----

Original data is available in the directory ``data/``. The naming structure is

as follows:

    - prefix "image"

    - nr of the sample

    - nr of the field inside the sample

    - channel (protein, dna, rois, or rois2).

For example, the file ``image_25_00_protein.png`` is from image 25, index 0,

and is the protein channel. The ROI files are the result of human labeling.

See the manuscript for details on data acquisition.

Source code

-----------

The source code is split into two directories

- ``nets`` this is the library code, which is useful to adapt to new projects.

- ``reproduce`` in this directory, you will find all the necessary code to

  reproduce all figures in the paper (including supplemental material).

In addition, there are two helper scripts:

predict_image.py

    ``predict_image.py`` returns a prediction for a single input file. It takes

    two arguments, which should be the image files for the DNA and histone

    channels, respectively::

        python predict_image.py ../data/image_00_00_dna.png ../data/image_00_00_protein.png

    If it cannot find a model to load, then it runs the ``create_model.py`` script.

create_model.py

    This needs to be run once to learn the model from the data. It will look at

    the files in the directory ``../data/`` for its input. Running this step

    may require a lot of memory! If you do not have enough in your machine, you

    can adjust the ``--n-estimators`` parameter to a smaller value.

We only recommend that you use our model trained on our data if your images are

very similar to ours. Otherwise, you can still use our software, but we

recommend you provide our system some training data.

The simplest way to reuse the software is to replace the images in the

``data/`` directory by your own (using the same naming format:

``prefix_dna.png``, ``prefix_protein.png``, and ``prefix_rois.png`` forming a

triple). Note that the ``_rois.png`` image should be a labeled image (i.e.,

pixels with value 0 correspond to background, pixels with value 1 correspond to

the first area of interest, pixels with value 2 to the second area of interest,

...).

Alternatively, there is a detailed tutorial on how to adapt the library for new

uses in the file ``tutorial.rst``.

Dependencies

~~~~~~~~~~~~

For running on your own data:

- numpy

- scikit-learn

- mahotas

Additionally, for reproducing our experiments:

- jug

- pandas

- matplotlib

- seaborn

The file ``requirements.txt`` in the ``source-code`` directory lists all the

requirements. If you have permission to do so, running the following command

inside that directory should install all dependencies::

    pip install -r requirements.txt

If this fails, try::

    sudo pip install -r requirements.txt

Reproducing the paper

~~~~~~~~~~~~~~~~~~~~~

The results of the paper can be reproduced on a Unix-like machine by running

the ``reproduce.sh`` script inside ``source-code/reproduce`` after having

installed the the requirements as listed above.

To use multiple processors, edit this script and set the value of the

``NR_CPUS`` variable.

Files:

jugfile.py

    This is the central file which runs the whole analysis

compare.py

    This script performs the reported comparison between the two operators

bernsen_thresholding.py

    This script evaluates Bernsen thresholding for different sets of parameters

compare-example.py

    This builds a side-by-side Figure showing differences between operators.

draw-composites.py

    This draws composite images for all inputs images