Overview

Overview#

snazzy_processing is a Python package to automate the extraction of primary data: Ventral Nerve Cord (VNC) length and signal intensity from fluorescence imaging of Drosophila embryos. The package is an image processing pipeline, that can be divided intro three main stages:

  • Crop raw data containing many samples into smaller movies (one per sample)

  • Calculate signal intensity inside ROIs

  • Measure the VNC length

Use the jupyter notebook snazzy-processing-pipeline.ipynb to run the pipeline.

The data processing requires a .tif file. There is built in support for converting .nd2 files to .tif. This means that you can feed either .tif or .nd2 files into the pipeline. If your raw data is in another format, you must first convert it to .tif. ImageJ for example provides several plugins to convert files to tif, including the excellent BioFormats extension.

Before running the pipeline, which is the last cell of the jupyter notebook, we must determine where to crop the raw data to separate samples.

Thresholding is used to distinguish each sample from its background and create bounding boxes. Because thresholding depends on image histograms, and these histograms might vary between movies, some manual adjustment may be necessary. Inspect where the bounding boxes will be created in the jupyter notebook. They should cover the entire sample. If there is a bounding box that covers more than one specimen (e.g, samples are touching each other), those specimens must be ignored or further manually processed. Boxes that contain several samples cannot be used directly because the resulting ROI will not match a single sample and the length & activity data will be wrong.

After the bounding boxes are determined, you can select which embryos to include in the analysis and run the pipeline. By default, the methods in the pipeline will not overwrite any data. If data for a given sample is found in the output directory, it will simply skip that sample. If you want to recalculate any data, first remove or rename the current files.

Refer to the other documentation pages for a description of the pipeline steps: