Trynka Labtglow-core
v0.1.4
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Tglow: Core Python component of the tglow imaging pipeline

tglow-core is the Python core component of the Tglow high-content imaging (HCI) analysis pipeline. It provides utilities to index and read multi-well plate images and parsers for PerkinElmer (Opera Phenix / Operetta) exports. The package is used by the tglow-pipeline workflows to load, preprocess and write OME-TIFF images arranged in the common /plate/row/col/field.ome.tiff (CYZX) layout.

Key features

  • Read and write CYZX / ZYX / YX image arrays via AICSImageReader / AICSImageWriter (wrappers around aicsimageio)
  • Parse Revity/PerkinElmer Index.xml exports (PerkinElmerParser) and convert to a simple, Python-friendly index
  • Convert large Revity/PerkinElmer exports to a much lower number of /plate/row/col/field.ome.tiff files
  • Index and query plate/row/col/field image layouts using an ImageQuery object
  • Utilities for registration, flatfield correction and numeric conversions designed to work with tglow-pipeline

Installation

I recommend installing the published PyPI release where possible:

pip install tglow-core

To install the latest development version from the repository (editable install):

git clone https://github.com/TrynkaLab/tglow-core
cd tglow-core
pip install -e .

Basic usage

Build an index from a PerkinElmer export and read a single image:

from tglow.io.tglow_io import PerkinElmerRawReader
from tglow.io.image_query import ImageQuery

reader = PerkinElmerRawReader('path/to/Index.xml', '/data/exports')
iq = ImageQuery.from_plate_well('plate1', 'A01')
image = reader.read_image(iq)  # returns a numpy array

Read and write an OME-TIFF stack organized by plate/row/col/field:

from tglow.io.tglow_io import AICSImageReader, AICSImageWriter
from tglow.io.image_query import ImageQuery

reader = AICSImageReader('/data/plates')
writer = AICSImageWriter('/output/plates')
iq = ImageQuery('plate1', 1, 1, 'field001')
stack = reader.read_stack(iq)
writer.write_stack(stack, iq)

Notes and migration to BioIO

This package currently wraps aicsimageio. As that project has been superseded by newer tooling, consider migrating to bioio or equivalent in future releases.

Known issues

There is a known issue with BaSiCpy (https://github.com/peng-lab/BaSiCPy/issues/162). This requires using specific, older versions of hyperactive and gradient-free-optimizers, which can in turn require an older pandas version. The same goes for aicsimageio. The dependency chain can be inconvenient; migration to BioIO libraries is planned as newer releases become available.

Acknowledgements

  • Martin Prete: initial XML parsing code adapted for this project

References