Trynka Labtglow-core
  • v0.2.0
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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 bioio)
    • 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
    • Render a self-contained HTML QC report for a tglow-pipeline run (tglow.qc.render.build_report), covering general stats, registration, flatfield, deconvolution, intensity, scaling and debris tabs

    Channel numbers are 0-indexed throughout this package, including in the ch<N>__ feature columns produced for tglow-pipeline and in the QC report.

    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)

    Acknowledgements

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

    References