cellphe 0.0.3

CellPhe: Toolkit for cell phenotyping from time-lapse videos

CellPhe

CellPhe provides functions to phenotype cells from time-lapse videos and accompanies the paper:
Wiggins, L., Lord, A., Murphy, K.L. et al.
The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition.
Nat Commun 14, 1854 (2023).
https://doi.org/10.1038/s41467-023-37447-3

The Python package is a port of the original R implementation.

Installation

You can install the latest version of CellPhe from PyPi with:

pip install cellphe

Example

An example dataset to demonstrate CellPhe’s capabilities is hosted on Dryad in the archive example_data.zip and comprises 3 parts:

• The time-lapse stills as TIFF images (05062019_B3_3_imagedata)
• Existing pre-extracted features (05062019_B3_3_Phase-FullFeatureTable.csv)
• Region-of-interest (ROI) boundaries already demarked in ImageJ format (05062019_B3_3_Phase)

These should be extracted into a suitable location before proceeding with the rest of the tutorial.

from cellphe import copy_features, extract_features, time_series_features

The first step is to prepare a dataframe containing metadata and any pre-existing attributes. If PhaseFocus Livecyte or Trackmate software has been used to generate the region-of-interest (ROI) files, then a helper function is available to create the required metadata format: copy_features. The dataframe format comprises each row corresponding to a cell tracked in a given frame, indexed by columns FrameID and CellID which contain numerical identifiers (NB: FrameID must be in ascending chronological order). The only other required field is ROI_filename, which specifies the filename of the ROI file corresponding to the frame-cell combination. Any features can be provided in additional columns, copyFeatures returns volume and sphericity from PhaseFocus software.

The example below creates the metadata dataframe from a PhaseFocus experimental setup, only including cells that were tracked for at least 50 frames.

min_frames = 50
input_feature_table = "05062019_B3_3_Phase-FullFeatureTable.csv"
feature_table = copy_features(input_feature_table, min_frames, source="Phase")

In addition to any pre-calculated features, the extract_features() function generates 74 descriptive features for each cell on every frame using the frame images and pre-generated cell boundaries, based on size, shape, texture, and the local cell density. The output is a dataframe comprising the FrameID, CellID, and ROI_filename identifying columns, the 74 features as columns, and any additional features that may be present (such as from copy_features()) in further columns. The program expects frames to be named according to the scheme <experiment name>-<frameid>.tif, where <frameid> is a 4 digit zero-padded integer corresponding to the FrameID column, and located in the frame_folder directory, while ROI files are named according to the ROI_filename column and located in the roi_folder directory.

roi_folder = "05062019_B3_3_Phase"
image_folder = "05062019_B3_3_imagedata"
new_features = extract_features(feature_table, roi_folder, image_folder, framerate=0.0028)

Variables are calculated from the time series for any pre-existing features as well as the output of extract_features(), providing both summary statistics and indicators of time-series behaviour at different levels of detail obtained via wavelet analysis. 15 summary scores are calculated for each feature, in addition to the cell trajectory, thereby resulting in a default output of 1081 features (15x72 + 1). These are output in the form of a dataframe with the first column being the CellID used previously.

tsvariables = time_series_features(new_features)