Multivariate Technique for Detecting Variations in High-Dimensional Imagery
<p dir="ltr">The field of immunology requires refined techniques to identify detailed cellular variance in high-dimensional images. Current methods mainly capture general immune cell proportion variations and often overlook specific deviations in individual patient samples from group...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513543077888000 |
|---|---|
| author | Ridwan A. Sanusi (12394171) |
| author2 | Jimoh Olawale Ajadi (15195955) Saddam Akber Abbasi (7908302) Taofik O. Dauda (21840350) Nurudeen A. Adegoke (11763932) |
| author2_role | author author author author |
| author_facet | Ridwan A. Sanusi (12394171) Jimoh Olawale Ajadi (15195955) Saddam Akber Abbasi (7908302) Taofik O. Dauda (21840350) Nurudeen A. Adegoke (11763932) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ridwan A. Sanusi (12394171) Jimoh Olawale Ajadi (15195955) Saddam Akber Abbasi (7908302) Taofik O. Dauda (21840350) Nurudeen A. Adegoke (11763932) |
| dc.date.none.fl_str_mv | 2024-04-09T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3386591 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multivariate_Technique_for_Detecting_Variations_in_High-Dimensional_Imagery/29714354 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Immunology Information and computing sciences Machine learning Dimensionality reduction high-dimension data image monitoring multivariate Shewhart control chart quality control in healthcare random projection methods Immune system Sparse matrices Monitoring Medical services Vectors Control charts Quality control |
| dc.title.none.fl_str_mv | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The field of immunology requires refined techniques to identify detailed cellular variance in high-dimensional images. Current methods mainly capture general immune cell proportion variations and often overlook specific deviations in individual patient samples from group baseline. We introduce a simple technique that integrates Hotelling’s T2 statistic with random projection (RP) methods, specifically designed to identify changes in immune cell composition in high-dimensional images. Uniquely, our method provides deeper insights into individual patient samples, allowing for a clearer understanding of group differences. We assess the efficacy of the technique across various RPs: Achlioptas (AP), plus-minus one (PM), Li, and normal projections (NP), considering shift size, dimension reduction, and image dimensions. Simulations reveal variable detection performances across RPs, with PM outperforming and Li lagging. Practical tests using single-cell images of basophils (BAS) and promyelocytes (PMO) emphasise their utility for individualised detection. Our approach elevates high-dimensional image data analysis, particularly for identifying shifts in immune cell composition. This breakthrough potentially transforms healthcare practitioners’ cellular interpretation of the immune landscape, promoting personalised patient care, and reshaping the discernment of diverse patient immune cell samples.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3386591" target="_blank">https://dx.doi.org/10.1109/access.2024.3386591</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_97455ee5dd601f2aefc19f83f19cabe3 |
| identifier_str_mv | 10.1109/access.2024.3386591 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29714354 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multivariate Technique for Detecting Variations in High-Dimensional ImageryRidwan A. Sanusi (12394171)Jimoh Olawale Ajadi (15195955)Saddam Akber Abbasi (7908302)Taofik O. Dauda (21840350)Nurudeen A. Adegoke (11763932)Biological sciencesBioinformatics and computational biologyBiomedical and clinical sciencesImmunologyInformation and computing sciencesMachine learningDimensionality reductionhigh-dimension dataimage monitoringmultivariate Shewhart control chartquality control in healthcarerandom projection methodsImmune systemSparse matricesMonitoringMedical servicesVectorsControl chartsQuality control<p dir="ltr">The field of immunology requires refined techniques to identify detailed cellular variance in high-dimensional images. Current methods mainly capture general immune cell proportion variations and often overlook specific deviations in individual patient samples from group baseline. We introduce a simple technique that integrates Hotelling’s T2 statistic with random projection (RP) methods, specifically designed to identify changes in immune cell composition in high-dimensional images. Uniquely, our method provides deeper insights into individual patient samples, allowing for a clearer understanding of group differences. We assess the efficacy of the technique across various RPs: Achlioptas (AP), plus-minus one (PM), Li, and normal projections (NP), considering shift size, dimension reduction, and image dimensions. Simulations reveal variable detection performances across RPs, with PM outperforming and Li lagging. Practical tests using single-cell images of basophils (BAS) and promyelocytes (PMO) emphasise their utility for individualised detection. Our approach elevates high-dimensional image data analysis, particularly for identifying shifts in immune cell composition. This breakthrough potentially transforms healthcare practitioners’ cellular interpretation of the immune landscape, promoting personalised patient care, and reshaping the discernment of diverse patient immune cell samples.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3386591" target="_blank">https://dx.doi.org/10.1109/access.2024.3386591</a></p>2024-04-09T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3386591https://figshare.com/articles/journal_contribution/Multivariate_Technique_for_Detecting_Variations_in_High-Dimensional_Imagery/29714354CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/297143542024-04-09T06:00:00Z |
| spellingShingle | Multivariate Technique for Detecting Variations in High-Dimensional Imagery Ridwan A. Sanusi (12394171) Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Immunology Information and computing sciences Machine learning Dimensionality reduction high-dimension data image monitoring multivariate Shewhart control chart quality control in healthcare random projection methods Immune system Sparse matrices Monitoring Medical services Vectors Control charts Quality control |
| status_str | publishedVersion |
| title | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| title_full | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| title_fullStr | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| title_full_unstemmed | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| title_short | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| title_sort | Multivariate Technique for Detecting Variations in High-Dimensional Imagery |
| topic | Biological sciences Bioinformatics and computational biology Biomedical and clinical sciences Immunology Information and computing sciences Machine learning Dimensionality reduction high-dimension data image monitoring multivariate Shewhart control chart quality control in healthcare random projection methods Immune system Sparse matrices Monitoring Medical services Vectors Control charts Quality control |