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Single cell quantification

Module for single-cell data extraction given a segmentation mask and multi-channel image. The CSV structure is aligned with histoCAT output.

CommandSingleCellExtraction.py:

  • --masks Paths to where masks are stored (Ex: ./segmentation/cellMask.tif) -> If multiple masks are selected the first mask will be used for spatial feature extraction but all will be quantified

  • --image Path to image(s) for quantification. (Ex: ./registration/*.h5) -> works with .h(df)5 or .tif(f)

  • --output Path to output directory. (Ex: ./feature_extraction)

  • --channel_names csv file containing the channel names for the z-stack (Ex: ./my_channels.csv)

  • --mask_props Space separated list of additional metrics to be calculated for every mask. This is intended for metrics that depend only on the cell mask. If the metric depends on signal intensity, use --intensity-props instead. See list at https://scikit-image.org/docs/dev/api/skimage.measure.html#regionprops

  • --intensity_props Space separated list of additional metrics to be calculated for every marker separately. By default only mean intensity is calculated. If the metric doesn't depend on signal intensity, use --mask-props instead. See list at https://scikit-image.org/docs/dev/api/skimage.measure.html#regionprops

    Currently, the following additional properties can be specified:

    • --intensity_props gini_index : The Gini index calculates a single number between 0 and 1, representing how unequal the signal is distributed in each region. See https://en.wikipedia.org/wiki/Gini_coefficient for more information.

    • --intensity_props intensity_median : Will calculate the median of intensity values per labeled object in the mask.

    • --intensity_props intensity_sum : Will calculate the sum of intensity values per labelled object in the mask. This can be useful if you want to count RNA molecules from FISH based images for example.

    • --intensity_props intensity_std : Will calculate the standard deviation of intensity values per labeled object in the mask.

      Further available are GLCM-derived graycoprops (see https://scikit-image.org/docs/stable/api/skimage.feature.html). Currently these metrices can only be calculated for one angle and distance at a time.

    • --intensity_props contrast : Will calculate the glcm derived symmetric and normalized contrast of intensity values per labeled object in the mask.

    • --intensity_props dissimilarity : Will calculate the glcm derived symmetric and normalized dissimilarity of intensity values per labeled object in the mask.

    • --intensity_props homogeneity : Will calculate the glcm derived symmetric and normalized homogeneity of intensity values per labeled object in the mask.

    • --intensity_props energy : Will calculate the glcm derived symmetric and normalized energy of intensity values per labeled object in the mask.

    • --intensity_props correlation : Will calculate the glcm derived symmetric and normalized correlation of intensity values per labeled object in the mask.

    • --intensity_props ASM : Will calculate the glcm derived symmetric and normalized ASM of intensity values per labeled object in the mask.

      Parameters for GLCM calculation

    • --glcm_angle Angle in radians used for calculating the GLCM per label. Default is 0 radians.

    • --glcm_distance Distance in pixels used for calculating the GLCM per label. Default is 1 pixel.

Run script

python CLI.py --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv

Main developer

Denis Schapiro (https://github.com/DenisSch)

Joshua Hess (https://github.com/JoshuaHess12)

Jeremy Muhlich (https://github.com/jmuhlich)