Automated

This one example workflow from the Cell Profiler(CP)  Examples . CP is commonly used to count cells or other objects as well as percent-positives, by measuring the per-cell staining intensity. This pipeline shows how to do both of these tasks, and demonstrates how various modules may be used to accomplish the same result. 

In a few words, it used the IdentifyPrimaryObject module of CellProfiler to detect nuclei from a channel (e.g DAPI), then again the same module on another channel to detect another probe (e.g some particular histone)  .

Then objects (nuclei) are related to the second object (Histone), to create a parent child-relation ship: where nuclei can have histone has child. Nuclei are then filtered according to the property of having histone (positive) or not having histone (negtiveobject) related to them.  If needed, nuclei can be expanded in order to include touching object rather than object inside only.

The percentage of positive nuclei vs total number of nuclei can then be computed using the CalculateMath Module.

This is a Jupyter notebook demonstrating the run of a code from IDR data sets by loading a CellProfiler Pipeline 

The example here is applied on real data set, but does not correspond to a biological question. It aims to demonstrate how to create a jupyter notebook to process online plates hosted in the IDR.

It reads the plate images from the IDR.

It loads the CellProfiler Pipeline and replace the reading modules used to read local files from this defaults pipeline by module allowing to read data remotely accessible.

It creates a CSV file and displays it in the notebook.

It makes some plot with Matplotlib.

Python/C++ port of the ImageJ extension TurboReg/StackReg written by Philippe Thevenaz/EPFL.

A python extension for the automatic alignment of a source image or a stack (movie) to a target image/reference frame.

This component can be used to find moving foreground features, which can be a powerful way to suppress false background detections in subsequent tracking steps.

set time window, and standard deviations above background for foreground time window should be more than 2x larger than time taken for a feature to traverse a pixel (NB. total window is 2x half-width +1) moving foreground identified by intensity increase relative to background average (i.e. median) for a pixel over a given time window "soft" segmentation, yielding foreground probability related to excess intensity (in standard deviations) over background level crude Anscombe transform applied to data to stabilize the variance