Quality control of perturbations.
<p>(A) Comparison of the original (white) and perturbed cell masks (colored according to perturbation strength: blue, red, green, yellow) based on mask borders on a randomly selected image slide. (B) Kernel-density estimation (KDE) for single-cell expression residuals between original and pert...
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2025
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| _version_ | 1852016587623628800 |
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| author | Matthias Bruhns (22250913) |
| author2 | Jan T. Schleicher (22250916) Maximilian Wirth (16868202) Marcello Zago (22250919) Sepideh Babaei (737927) Manfred Claassen (2697070) |
| author2_role | author author author author author |
| author_facet | Matthias Bruhns (22250913) Jan T. Schleicher (22250916) Maximilian Wirth (16868202) Marcello Zago (22250919) Sepideh Babaei (737927) Manfred Claassen (2697070) |
| author_role | author |
| dc.creator.none.fl_str_mv | Matthias Bruhns (22250913) Jan T. Schleicher (22250916) Maximilian Wirth (16868202) Marcello Zago (22250919) Sepideh Babaei (737927) Manfred Claassen (2697070) |
| dc.date.none.fl_str_mv | 2025-09-15T17:51:36Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013350.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Quality_control_of_perturbations_/30131688 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | Quality control of perturbations. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(A) Comparison of the original (white) and perturbed cell masks (colored according to perturbation strength: blue, red, green, yellow) based on mask borders on a randomly selected image slide. (B) Kernel-density estimation (KDE) for single-cell expression residuals between original and perturbed data. (C) KDE for median area changes of cells. (D) KDE for the mean absolute error between true and perturbed feature correlation matrices. (E) KDE for the results of the classifier 2-sample test.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_6cdf87f2dacbfa641f012efc8bf6083d |
| identifier_str_mv | 10.1371/journal.pcbi.1013350.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30131688 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Quality control of perturbations.Matthias Bruhns (22250913)Jan T. Schleicher (22250916)Maximilian Wirth (16868202)Marcello Zago (22250919)Sepideh Babaei (737927)Manfred Claassen (2697070)BiophysicsBiochemistryCell BiologyGeneticsMolecular BiologyPhysiologyDevelopmental BiologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifieduses affine transformationssmaller neighborhood sizesgenerate expression profilescontrolled perturbation conditionsdefines cell boundariescell imaging technologiessegmentation inaccuracies propagatesegmentation error leadingconsidering segmentation inaccuraciesmitigate spurious resultsaccurate cell segmentationprobabilistic modeling frameworkdownstream analysis taskscell levelsegmentation errorssegmentation algorithmsquality segmentationresults highlightvariations inducedunsupervised ktissue organizationthereby enablingnotable misclassificationshigher levelsfeature spaceensuring highdownstream analysesdeeper understandingclustering analysesapproach mimicsanalytical pipelinesallowing usadversely impactedadvancements rely<p>(A) Comparison of the original (white) and perturbed cell masks (colored according to perturbation strength: blue, red, green, yellow) based on mask borders on a randomly selected image slide. (B) Kernel-density estimation (KDE) for single-cell expression residuals between original and perturbed data. (C) KDE for median area changes of cells. (D) KDE for the mean absolute error between true and perturbed feature correlation matrices. (E) KDE for the results of the classifier 2-sample test.</p>2025-09-15T17:51:36ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013350.g002https://figshare.com/articles/figure/Quality_control_of_perturbations_/30131688CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301316882025-09-15T17:51:36Z |
| spellingShingle | Quality control of perturbations. Matthias Bruhns (22250913) 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 |
| status_str | publishedVersion |
| title | Quality control of perturbations. |
| title_full | Quality control of perturbations. |
| title_fullStr | Quality control of perturbations. |
| title_full_unstemmed | Quality control of perturbations. |
| title_short | Quality control of perturbations. |
| title_sort | Quality control of perturbations. |
| topic | 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 |