napari_bigfish.bigfishapp module

class napari_bigfish.bigfishapp.BigfishApp[source]

Bases: PyQt5.QtCore.QObject

Application model for the napari FISH-spot detection widget, that runs the bigfish gaussian background correction and spot detection.

activateFindThreshold()[source]

activateRemoveDuplicates()[source]

alphaSignal[source]

betaSignal[source]

countSpotsPerCellAndEnvironment(cytoplasmLabels, nucleiLabels)[source]

Counts the number of spots in the image and stores it in the attribute nrOfCells. Creates the cellLabelOfSpot list, that contains the cell-label for each spot and the nucleiLabelOfSpot list, that contains True for all spots that are within a nucleus.

Parameters

cytoplasmLabels (numpy.ndarray) – The cell-labels
nucleiLabels (numpy.ndarray) – The nuclei-mask or labels

createEmptySpotCountReport(inputPath)[source]

Create a csv-file, containing only the column headings, for the spot-count-report and return the path to the file. The file is written into a subfolder results of the input folder.

Parameters: inputPath – The path to an input image

deactivateFindThreshold()[source]

deactivateRemoveDuplicates()[source]

decomposeDenseRegions(scale)[source]

Run the decomposition of the dense regions.

Parameters: scale (2 or 3-tupel of float) – The scale (voxel size) of the image in the z, y and x dimensions in nm.

decomposeRadiusSignal[source]

detectSpots(scale)[source]

Run the spot detection step with or without automatic threshold detection.

Parameters: scale (2 or 3-tupel of float) – The scale (voxel size) of the image in the z, y and x dimensions in nm.

findThresholdSignal[source]

gammaSignal[source]

getAlpha()[source]

getBeta()[source]

getData()[source]

getDecomposeRadiusXY()[source]

getDecomposeRadiusZ()[source]

getDecomposeSpotRadius()[source]

Answer the spot radius for the decomposition of dense regions.

Return type: 2 or 3-tupel of float

getGamma()[source]

getRadiusXY()[source]

getRadiusZ()[source]

getResult()[source]

getScale(scale)[source]

Answer the scale (voxel-size) in nm in the different dimensions

Return type: 2 or 3-tupel of float

getSigmaXY()[source]

getSigmaZ()[source]

getSpotCountPerCellAndEnvironment()[source]

Returns a table containing the spot-count for each cell, with the number of cells within the nucleus, outside of the nucleus and the total number.

Return type: list of lists

getSpotRadius()[source]

Return the spot radius in the z, y and x-dimension

Return type: 2 or 3-tupel of float

getSpots()[source]

getThreshold()[source]

progressSignal[source]

radiusSignal[source]

removeDuplicatesSignal[source]

reportSpotCounts(inputPath, outputPath)[source]

Write a csv-file with the spot-counts.

Parameters

inputPath – The path of the input image will be reported in the csv-file
outputPath – The directory into which the csv-file will be written

reportSpots(inputPath)[source]

Write a csv-file with the coordinates of the detected spots. The file can be opened by napari as a points-layer.

Parameters: inputPath – The past to the input images; the file will be written into a subdirectory “spots” of that directory.

runBatch(scale, inputImages, cellLabels=None, nucleiMasks=None, subtractBackground=False, decomposeDenseRegions=False)[source]

Run the processing in batch-mode on the input images.

Parameters

scale (3-tupel of floats) – A tupel with the scales (voxel-sizes) of the images in nm for the z, y and x dimensions
inputImages – A list of paths to the input images
cellLabels – An optional list of paths to the cell label images
nucleiMasks – An optional list of paths to the nuclei mask images
subtractBackground – A boolean telling wether to subtract the background before the analysis
decomposeDenseRegion – A boolean telling wether to decompose dense regions for the spot detection

setAlpha(alpha)[source]

setBeta(beta)[source]

setData(data)[source]

setDecomposeRadiusXY(radius)[source]

setDecomposeRadiusZ(radius)[source]

setGamma(gamma)[source]

setProgress(progress)[source]: Set the current progress and send the progressSignal with the current and the max. progress.

setProgressMax(max)[source]: Set the max. progress and send the progressSignal with the current and the max. progress.

setRadiusXY(radius)[source]

setRadiusZ(radius)[source]

setSigmaXY(sigmaXY)[source]

setSigmaZ(sigmaZ)[source]

setThreshold(threshold)[source]

shallFindThreshold()[source]

shallRemoveDuplicates()[source]

sigmaSignal[source]

subtractBackground()[source]: Apply the gaussian background removal to the data of the application. The resulting image is stored in the result-attribute.

thresholdSignal[source]