-
Notifications
You must be signed in to change notification settings - Fork 0
Home
wgiese edited this page Nov 6, 2019
·
52 revisions
This is a short overview on the scripts an analysis provided in this repository. The analysis tools are written in Python 3.6, the following python modules need to be installed:
- numpy
- seaborn
- matplotlib
- pandas as pd
- scipy
- skimage
The workflow is shown in the diagram above. As input the following files need to be provided
mouse_name | type | analysis_file_vessels | analysis_file_macrophages | x_resolution [um] | y_resolution [um] | vessel_channel | macrophage_channel_1 | macrophage_channel_2 |
---|---|---|---|---|---|---|---|---|
mouse_1 | 4 weeks | vessel_file1.tif | macrophages_1.csv | 400 | 400 | 0 | 1 | 2 |
mouse_1 | 4 weeks | vessel_file2.tif | macrophages_2.csv | 400 | 400 | 0 | 1 | 2 |
mouse_2 | 2 weeks | vessel_file3.tif | macrophages_3.csv | 300 | 300 | 1 | 0 | 2 |
The parameter file has the following content
{
"data_directory": "/directory/to/your/data/",
"meta_data_file_1" : "meta_file_1",
"subfolder_vessel_images_1" : "subfolder_1",
"channels_dataset_1" : ["channel_name_1"],
"meta_data_file_2" : "meta_file_1",
"subfolder_vessel_images_2" : "subfolder_2",
"channels_dataset_2" : ["channel_name_1","channel_name_2"]
}
Here you can adjust folder names and channel names etc.
The following features can be calculated
- nearest distance of macrophages to the vessels
- nearest distance of macrophages to thick vessels (here thick is defined by a threshold on the vessel area)
- nearest distance of macrophages to thin vessels (here thin is defined by a threshold on the vessel area)
- normalized distance of macrophages to the vessels (here the distances are normalized to the distances that would stem from a completely random distribution)
- occupancy of vessels
- n-nearest neighbour ditances among macrophages