JIMG_int – Python library for marker intensity and distribution analysis

Author: Jakub Kubiś

Description

📚 Table of Contents

• 1.Installation
• 2.Documentation
• 3.Example pipelines
  • 3.1. Marker intensity features extraction
    • 3.1.1 Adjusting parameters and image loading
    • 3.1.2 ROI mask loading
    • 3.1.3 Normalization mask loading
    • 3.1.4 Extracting intensity values
    • 3.1.5 Saving intensity data
    • 3.1.6 APerforming analysis pipeline for the subsequent image
    • 3.1.7 Combining the experimental data
  • 3.2. Analyzing the intensity and spatial distribution of the marker
    • 3.2.1 Loading experimental data
    • 3.2.2 Quantitative analysis of marker intensity and spatial distribution
    • 3.2.3 Visualizing the data using a histogram distribution

1. Installation

In command line write:

pip install jimg-int

2. Documenation

Documentation for classes and functions is available here 👉 Documentation 📄

3. Example pipelines

If you want to run the examples, you must download the test data. To do this, use:

from jimg_ncd.nuclei import test_data

test_data()

3.1 Marker intensity features extraction

3.1.1 Adjusting parameters and image loading

from jimg_int.intensity import FeatureIntensity

# Select intenity are data for 1st Image - healthy

# initiate class
fi = FeatureIntensity()

# check current metadata
fi.current_metadata

# if required, change parameters
fi.set_projection(projection="avg")

fi.set_correction_factorn(factor=0.2)

# fi.set_scale(scale = 0.5)
# fi.set_selection_list(rm_list = [2,5,6,7])
# OR
# load JIMG project where scale and rm_lis is set in project metadata
# fi.load_JIMG_project_(path = '')
# for more information go to: https://github.com/jkubis96/JIMG
# rm_list and scale can be omitted

# load image
fi.load_image_3D(path="test_data/intensity/ctrl/image.tiff")

# or 1D image after projection, be sure that image was not adjusted, for analysis should be use !RAW! image
# fi.load_image_(path)

Analysed image projection (after projection with JIMG)

• input image in this case is raw 3D-image in *.tiff format

3.1.2 ROI mask loading

fi.load_mask_(path = 'test_data/intensity/ctrl/mask_1.png')

Analysed image region mask

3.1.3 Normalization mask loading

fi.load_normalization_mask_(path = 'test_data/intensity/ctrl/background_1.png')

Normalization region mask (reversed)

3.1.4 Extracting intensity values

# strat calculations
fi.run_calculations()


# get results
results = fi.get_results()

3.1.5 Saving intensity data

# save results for further analysis, ensuring each feature 
# is stored in a separate directory (single directory 
# should contain data with the same 'feature_name'),
# this setup allows running fi.concatenate_intensity_data() 
# in the specific directory of each feature
# while preventing errors from incorrect feature concatenation

fi.save_results(path = os.getcwd(), 
             mask_region = 'brain', 
             feature_name = 'Feature1', 
             individual_number = 1, 
             individual_name = 'CTRL')

3.1.6 Performing analysis pipeline for the subsequent image

• Apply steps 3.1.1–3.1.5 to the subsequent image to support comparison analysis.

# Select intenity are data for 2st Image - disease

# initiate class
fi = FeatureIntensity()

fi.set_projection(projection="avg")

fi.set_correction_factorn(factor=0.2)

fi.load_image_3D(path="test_data/intensity/dise/image.tiff")

###############################################################################

fi.load_mask_(path="test_data/intensity/dise/mask_1.png")

###############################################################################

fi.load_normalization_mask_(path="test_data/intensity/dise/background_1.png")

###############################################################################

fi.run_calculations()

results = fi.get_results()

fi.save_results(
    path="",
    mask_region="brain",
    feature_name="Feature1",
    individual_number=1,
    individual_name="DISEASE",
)

Analysed image projection (after projection with JIMG)

• input image in this case is raw 3D-image in *.tiff format

Normalization region mask (reversed)

Analysed image region mask

3.1.7 Combining the experimental data

# concatenate data of experiment 1 & 2
fi.concatenate_intensity_data(directory="", name="example_data")

3.2 Analyzing the intensity and spatial distribution of the marker

3.2.1 Loading experimental data

import pandas as pd
from jimg_int.intensity import IntensityAnalysis

# initiate class
ia = IntensityAnalysis()

input_data = pd.read_csv("example_data_Feature1_brain.csv")

# check columns
input_data.head()

3.2.2 Quantitative analysis of marker intensity and spatial distribution

data = ia.df_to_percentiles(
    data=input_data,
    group_col="individual_name",
    values_col="norm_intensity",
    replication_col = 'individual_number',
    sep_perc=1,
)

stats = ia.get_stats(data, tested_value = 'avg')

3.2.3 Visualizing the data using a histogram distribution

results = ia.hist_compare_plot(
    data=stats, queue=["CTRL", "DISEASE"], p_adj=True, txt_size=20
)

Results of intensity comparison analysis (region under the mask)

results.savefig('example_results.svg', format = 'svg', dpi = 300, bbox_inches = 'tight')

Have fun JBS