Confusion matrices for phenotyping for all perturbation strengths.

Confusion matrices for phenotyping for all perturbation strengths.

<p>Only values ≥ 1 are shown. Color encodes the percentage of classified cells. For visual appearance, only off-diagonal elements are colored in.</p> <p>(TIF)</p>

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Bibliographic Details
Main Author:	Matthias Bruhns (22250913) (author)
Other Authors:	Jan T. Schleicher (22250916) (author), Maximilian Wirth (16868202) (author), Marcello Zago (22250919) (author), Sepideh Babaei (737927) (author), Manfred Claassen (2697070) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Cell Biology Genetics Molecular Biology Physiology Developmental Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified uses affine transformations smaller neighborhood sizes generate expression profiles controlled perturbation conditions defines cell boundaries cell imaging technologies segmentation inaccuracies propagate segmentation error leading considering segmentation inaccuracies mitigate spurious results accurate cell segmentation probabilistic modeling framework downstream analysis tasks cell level segmentation errors segmentation algorithms quality segmentation results highlight variations induced unsupervised k tissue organization thereby enabling notable misclassifications higher levels feature space ensuring high downstream analyses deeper understanding clustering analyses approach mimics analytical pipelines allowing us adversely impacted advancements rely
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