Table 1 QuantiFish workflow and programming.
From: Analysis tools to quantify dissemination of pathology in zebrafish larvae
Workflow | Steps | Measurement Name | Package Functions Used |
|---|---|---|---|
Load Image | Open image | Â | PIL Image Class |
| Â | Convert to array | Â | numpy.array |
Basic Statistics | Fetch minimum and maximum intensity values | Minimum, Maximum | numpy.min, numpy.max |
| Â | Apply user-defined threshold, set pixels below threshold to 0 | Â | Â |
| Â | Count positive pixels | Positive Pixels | numpy.count_nonzero |
| Â | Calculate integrated intensity | Integrated Intensity | numpy.sum |
Find Foci | Quantify local maxima (points of peak intensity) | Total Peaks | skimage.feature.peak_local_max |
| Â | Assign ID labels to confluent objects | Â | skimage.measure.label |
| Â | Quantify the number of pixels associated with each object | Total Foci | numpy.unique |
| Â | Filter foci list based on minimum size set by user | Â | Â |
| Â | Count objects larger than the size filter | Large Foci | numpy.sum |
| Â | Exclude staining in foci below size limit | Â | Â |
| Â | Quantify pixels in positive foci | Positive Pixels in Large Foci | numpy.count_nonzero |
| Â | Quantify integrated intensity within positive foci | Integrated Intensity in Large Foci | numpy.sum |
| Â | Count local maxima in filtered foci | Peaks in Large Foci | skimage.feature.peak_local_max |
Foci Statistics | Construct an array of statistics for each focus | Foci Area, Average, Max, Min and Integ. Intensity | skimage.measure.regionprops |
| Â | Record stats for each focus larger than the minimum size | Â | Â |
| Â | Generate list of centroid coordinates for large foci | Â | Â |
Fluor50 | Sort the focus statistics list by integrated intensity | Â | Â |
| Â | Calculate percent fluorescence contributed by each object | Foci Percent Intensity | Â |
| Â | Calculate cumulative percent fluorescence | Foci Cumulative Percent Intensity | numpy.cumsum |
| Â | Determine fluor50 by linear interpolation | Fluor50 | scipy.interpolate.interp1d |
Spatial Statistics | Create blank array the size of the original image | Â | numpy.zeros |
| Â | Plot foci centroid coordinates on blank array | Â | Â |
| Â | Split array into grid segments of user-defined size | Â | numpy.array_split |
| Â | Consider segment positive if any centroids are present | Â | numpy.max |
| Â | Count positive and total segments | Total Grid Boxes, Positive Grid Boxes | Â |
| Â | Generate convex polygon using centroid coordinates | Foci Polygon Area | scipy.spatial.ConvexHull |
| Â | Select centroids which generate the polygon | Â | scipy.spatial.ConvexHull.vertices |
| Â | Construct a distance matrix between selected points | Â | scipy.spatial.distance_matrix |
| Â | Find maximum distance between foci from matrix | IFDmax | scipy.spatial.distance.euclidean |
