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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.

Installation

  1. Download this repository and cd into the directory.
  2. pip install .

Run script

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

mcquant options:

  • --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.

Main developers

About

Quantification module for mcmicro

github.com/labsyspharm/mcmicro

Resources

Readme
Activity
Custom properties

Stars

11 stars

Watchers

2 watching

Forks

18 forks
Report repository

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Used by

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GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
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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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
Main developers
About
Quantification module for mcmicro
github.com/labsyspharm/mcmicro
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
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GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
Skip to content
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Repository files navigation
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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
Main developers
About
Quantification module for mcmicro
github.com/labsyspharm/mcmicro
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
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var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
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Repository files navigation
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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
Main developers
About
Quantification module for mcmicro
github.com/labsyspharm/mcmicro
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
Main developers
About
Quantification module for mcmicro
github.com/labsyspharm/mcmicro
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
Main developers
About
Quantification module for mcmicro
github.com/labsyspharm/mcmicro
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
Skip to content
BranchesTags
Repository files navigation
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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
Main developers
About
Quantification module for mcmicro
github.com/labsyspharm/mcmicro
Resources
Readme
Activity
Custom properties
Stars
11 stars
Watchers
2 watching
Forks
18 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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GitHub - labsyspharm/quantification: Quantification module for mcmicro · GitHub
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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.
Installation
  1. Download this repository and cd into the directory.
  2. pip install .
Run script
mcquant --masks ./segmentation/cellMask.tif ./segmentation/membraneMask.tif --image ./registration/Exemplar_001.h5 --output ./feature_extraction --channel_names ./my_channels.csv
mcquant options:
  • --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.
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Quantification module for mcmicro
github.com/labsyspharm/mcmicro
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