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-`atp-analysis`: Run the fibre type 1 vs type 2 analysis on...
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-`docs`: Generate documentation
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-`he-analysis`: Run the nuclei position analysis on HE and...
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-`sdh-analysis`: Run the mitochondiral analysis and...
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*`sdh-analysis`: Run the mitochondrial analysis and...
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*`he-analysis`: Run the nuclei position analysis on HE and...
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*`atp-analysis`: Run the fibre type 1 vs type 2 analysis on...
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*`tui`: Open Textual TUI.
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*`generate-docs`: Generate markdown version of usage...
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## `myoquant atp-analysis`
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## `myoquant sdh-analysis`
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Run the fibre type 1 vs type 2 analysis on ATP images.
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First input arguments and option are printed in stdout and all modules are imported. Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose , Cellpose is run or not and mask accordingly if binary mask is provided.
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Finally the ATP analysis is run with run_atp_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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Run the mitochondrial analysis and quantification on the image.
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First input arguments and option are printed in stdout and all modules are imported and latest SDH model is downloaded.
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Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose path, Cellpose is run or not and mask accordingly if binary mask is provided.
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Finally, the mitochondrial classification is run with run_sdh_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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**Usage**:
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```console
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$ myoquant atp-analysis [OPTIONS] IMAGE_PATH
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$ myoquant sdh-analysis [OPTIONS] IMAGE_PATH
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```
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**Arguments**:
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-`IMAGE_PATH`: The ATP image file path to analyse. [required]
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*`IMAGE_PATH`: The image file path to analyse. \[required]
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**Options**:
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-`--mask-path FILE`: The path to a binary mask to hide slide region during analysis. It needs to be of the same resolution as input image and only pixel marked as 1 will be analyzed.
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-`--cellpose-path FILE`: The pre-computed CellPose mask to use for analysis. Will run Cellpose if no path provided. Required as an image file.
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-`--output-path PATH`: The path to the folder to save the results. Will save in the same folder as input image if not specified.
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-`--intensity-threshold INTEGER RANGE`: Fiber intensity threshold to differenciate between the two fiber types. If not specified, the analysis will try to deduce it. [1<=x<=254]
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-`--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
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-`--channel INTEGER`: Image channel to use for the analysis. If not specified, the analysis will be performed on all three channels.
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-`--channel-first / --no-channel-first`: If the channel is the first dimension of the image, set this to True. False by default. [default: no-channel-first]
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-`--rescale-exposure / --no-rescale-exposure`: Rescale the image exposure if your image is not in the 0 255 forma, False by default. [default: no-rescale-exposure]
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-`--n-classes INTEGER RANGE`: The number of classes of cell to detect. If not specified this is defaulted to two classes. [default: 2; x<=10]
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-`--intensity-method TEXT`: The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'. [default: median]
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-`--erosion INTEGER RANGE`: Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo). Expressed in percentage of the cell radius [default: False; x<=45]
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-`--export-map / --no-export-map`: Export the original image with cells painted by classification label. [default: export-map]
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-`--export-stats / --no-export-stats`: Export per fiber and per nuclei stat table. [default: export-stats]
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-`--help`: Show this message and exit.
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-
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## `myoquant docs`
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Generate documentation
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*`--mask-path FILE`: The path to a binary mask to hide slide region during analysis. It needs to be of the same resolution as input image and only pixel marked as 1 will be analyzed.
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*`--cellpose-path FILE`: The pre-computed CellPose mask to use for analysis. Will run Cellpose if no path provided. Required as an image file.
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*`--output-path PATH`: The path to the folder to save the results. Will save in the current folder if not specified.
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*`--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
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*`--export-map / --no-export-map`: Export the original image with cells painted by classification label. \[default: export-map]
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*`--export-stats / --no-export-stats`: Export per fiber stat table. \[default: export-stats]
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*`--help`: Show this message and exit.
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## `myoquant he-analysis`
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Run the nuclei position analysis on HE and fluo images.
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First input arguments and option are printed in stdout and all modules are imported. Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose and stardist path, Cellpose and Stardist are run or not and mask accordingly if binary mask is provided.
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Finally, the nuclei analysis is run with run_he_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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**Usage**:
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```console
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$ myoquant docs [OPTIONS] COMMAND [ARGS]...
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$ myoquant he-analysis [OPTIONS] IMAGE_PATH
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```
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**Options**:
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**Arguments**:
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-`--help`: Show this message and exit.
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*`IMAGE_PATH`: The HE image file path to analyse. If using single channel images, this will be used as cytoplasm image to run CellPose. Please use the --fluo-nuc option to indicate the path to the nuclei single image to run Stardist. \[required]
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**Commands**:
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**Options**:
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-`generate`: Generate markdown version of usage...
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*`--mask-path FILE`: The path to a binary mask to hide slide region during analysis. It needs to be of the same resolution as input image and only pixel marked as 1 will be analyzed.
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*`--cellpose-path FILE`: The pre-computed CellPose mask to use for analysis. Will run Cellpose if no path provided. Required as an image file.
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*`--stardist-path FILE`: The pre-computed Stardist mask to use for analysis. Will run Stardist if no path provided. Required as an image file.
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*`--output-path PATH`: The path to the folder to save the results. Will save in the same folder as input image if not specified.
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*`--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
*`--prob-thresh FLOAT RANGE`: Probability Threshold for Stardist nuclei detection. \[default: 0.5; 0.5<=x<=1]
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*`--eccentricity-thresh FLOAT RANGE`: Eccentricity threshold value for a nucleus to be considered as internalized during nuclei classification. When very close to 1 almost all nuclei are considered as internalized. \[default: 0.75; 0<=x<=1]
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*`--export-map / --no-export-map`: Export the original image with cells painted by classification label. \[default: export-map]
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*`--export-stats / --no-export-stats`: Export per fiber and per nuclei stat table. \[default: export-stats]
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*`--fluo-nuc FILE`: The path to single channel fluo image for nuclei.
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*`--help`: Show this message and exit.
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###`myoquant docs generate`
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## `myoquant atp-analysis`
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Generate markdown version of usage documentation
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Run the fibre type 1 vs type 2 analysis on ATP images.
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First input arguments and option are printed in stdout and all modules are imported. Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose , Cellpose is run or not and mask accordingly if binary mask is provided.
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Finally, the ATP analysis is run with run_atp_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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**Usage**:
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```console
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$ myoquant docs generate [OPTIONS]
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$ myoquant atp-analysis [OPTIONS] IMAGE_PATH
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```
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**Options**:
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**Arguments**:
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-`--name TEXT`: The name of the CLI program to use in docs.
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-`--output FILE`: An output file to write docs to, like README.md.
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-`--help`: Show this message and exit.
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*`IMAGE_PATH`: The ATP image file path to analyse. \[required]
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## `myoquant he-analysis`
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**Options**:
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Run the nuclei position analysis on HE and fluo images.
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First input arguments and option are printed in stdout and all modules are imported. Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose and stardist path, Cellpose and Stardist are run or not and mask accordingly if binary mask is provided.
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Finally the nuclei analysis is run with run_he_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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*`--mask-path FILE`: The path to a binary mask to hide slide region during analysis. It needs to be of the same resolution as input image and only pixel marked as 1 will be analyzed.
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*`--cellpose-path FILE`: The pre-computed CellPose mask to use for analysis. Will run Cellpose if no path provided. Required as an image file.
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*`--output-path PATH`: The path to the folder to save the results. Will save in the same folder as input image if not specified.
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*`--intensity-threshold INTEGER RANGE`: Fiber intensity threshold to differenciate between the two fiber types. If not specified, the analysis will try to deduce it. [1<=x<=254]
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*`--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
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*`--channel INTEGER`: Image channel to use for the analysis. If not specified, the analysis will be performed on all three channels.
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*`--channel-first / --no-channel-first`: If the channel is the first dimension of the image, set this to True. False by default. \[default: no-channel-first]
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*`--rescale-exposure / --no-rescale-exposure`: Rescale the image exposure if your image is not in the 0 255 forma, False by default. \[default: no-rescale-exposure]
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*`--n-classes INTEGER RANGE`: The number of classes of cell to detect. If not specified this is defaulted to two classes. \[default: 2; x<=10]
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*`--intensity-method TEXT`: The method to use to compute the intensity of the cell. Can be either 'median' or 'mean'. \[default: median]
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*`--erosion INTEGER RANGE`: Perform an erosion on the cells images to remove signal in the cell membrane (usefull for fluo). Expressed in percentage of the cell radius \[default: False; x<=45]
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*`--export-map / --no-export-map`: Export the original image with cells painted by classification label. \[default: export-map]
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*`--export-stats / --no-export-stats`: Export per fiber and per nuclei stat table. \[default: export-stats]
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*`--help`: Show this message and exit.
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## `myoquant tui`
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Open Textual TUI.
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**Usage**:
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```console
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$ myoquant he-analysis [OPTIONS] IMAGE_PATH
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$ myoquant tui [OPTIONS]
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```
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**Arguments**:
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-`IMAGE_PATH`: The HE image file path to analyse. If using single channel images, this will be used as cytoplasm image to run CellPose. Please use the --fluo-nuc option to indicate the path to the nuclei single image to run Stardist. [required]
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**Options**:
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-`--mask-path FILE`: The path to a binary mask to hide slide region during analysis. It needs to be of the same resolution as input image and only pixel marked as 1 will be analyzed.
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-`--cellpose-path FILE`: The pre-computed CellPose mask to use for analysis. Will run Cellpose if no path provided. Required as an image file.
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-`--stardist-path FILE`: The pre-computed Stardist mask to use for analysis. Will run Stardist if no path provided. Required as an image file.
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-`--output-path PATH`: The path to the folder to save the results. Will save in the same folder as input image if not specified.
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-`--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
-`--prob-thresh FLOAT RANGE`: Probability Threshold for Stardist nuclei detection. [default: 0.5; 0.5<=x<=1]
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-`--eccentricity-thresh FLOAT RANGE`: Eccentricity threshold value for a nucleus to be considered as internalized during nuclei classification. When very close to 1 almost all nuclei are considered as internalized. [default: 0.75; 0<=x<=1]
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-`--export-map / --no-export-map`: Export the original image with cells painted by classification label. [default: export-map]
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-`--export-stats / --no-export-stats`: Export per fiber and per nuclei stat table. [default: export-stats]
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-`--fluo-nuc FILE`: The path to single channel fluo image for nuclei.
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-`--help`: Show this message and exit.
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*`--help`: Show this message and exit.
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## `myoquant sdh-analysis`
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## `myoquant generate-docs`
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Run the mitochondiral analysis and quantification on the image.
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First input arguments and option are printed in stdout and all modules are imported and latest SDH model is downloaded.
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Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose path, Cellpose is run or not and mask accordingly if binary mask is provided.
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Finally the mitochondiral classificaiton is run with run_sdh_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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Generate markdown version of usage documentation
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**Usage**:
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```console
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$ myoquant sdh-analysis [OPTIONS] IMAGE_PATH
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$ myoquant generate-docs [OPTIONS]
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```
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**Arguments**:
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-`IMAGE_PATH`: The image file path to analyse. [required]
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**Options**:
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-`--mask-path FILE`: The path to a binary mask to hide slide region during analysis. It needs to be of the same resolution as input image and only pixel marked as 1 will be analyzed.
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-`--cellpose-path FILE`: The pre-computed CellPose mask to use for analysis. Will run Cellpose if no path provided. Required as an image file.
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-`--output-path PATH`: The path to the folder to save the results. Will save in the current folder if not specified.
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-`--cellpose-diameter INTEGER`: Approximative single cell diameter in pixel for CellPose detection. If not specified, Cellpose will try to deduce it.
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-`--export-map / --no-export-map`: Export the original image with cells painted by classification label. [default: export-map]
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-`--export-stats / --no-export-stats`: Export per fiber stat table. [default: export-stats]
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-`--help`: Show this message and exit.
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*`--name TEXT`: The name of the CLI program to use in docs.
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*`--output FILE`: An output file to write docs to, like README.md.
Copy file name to clipboardExpand all lines: README.md
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@@ -34,6 +34,10 @@ I recommend using UV for python environment management. See [UV documentation](h
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## How to Use
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#### **🆕 NEW:** MyoQuant now comes with a Text User Interface (TUI) for an easier discoverability of the different CLI parameter. **For this you can run `myoquant tui` or `uv run myoquant tui` to launch the TUI.**
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To use the command-line tool, first activate your venv in which MyoQuant is installed: `source .venv/bin/activate` or simply install the package using UV.
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Then you can perform SDH or HE analysis. You can use the command `myoquant --help` or `uv run myoquant --help` to list available commands.
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For ATP Staining analysis, you can download this sample image: [HERE](https://www.lbgi.fr/~meyer/SDH_models/sample_atp.jpg)
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1. Example of successful SDH analysis output with: `myoquant sdh-analysis sample_sdh.jpg`
Copy file name to clipboardExpand all lines: src/myoquant/commands/run_he.py
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"""Run the nuclei position analysis on HE and fluo images.
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First input arguments and option are printed in stdout and all modules are imported. Then the input image is mask with the binary mask if provided.
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Then depending on the presence of cellpose and stardist path, Cellpose and Stardist are run or not and mask accordingly if binary mask is provided.
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Finally the nuclei analysis is run with run_he_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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Finally, the nuclei analysis is run with run_he_analysis() function and the results are saved in the output folder and some info are printed in stdout.
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