Temporal Multivariate Distribution Analysis of Cell Shape Descriptors

In early drug discovery and the study of the effects of new chemical compounds on cancer cells, the change in cell shape over time provides vital information about cell health. Live-cell image analysis systems can be used to extract cell-shape describing parameters of individual cells during exposur...

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Main Author: Krantz, Amanda
Format: Others
Language:English
Published: Umeå universitet, Institutionen för fysik 2021
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182264
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spelling ndltd-UPSALLA1-oai-DiVA.org-umu-1822642021-04-21T05:23:10ZTemporal Multivariate Distribution Analysis of Cell Shape DescriptorsengKrantz, AmandaUmeå universitet, Institutionen för fysik2021Mathematical AnalysisMatematisk analysIn early drug discovery and the study of the effects of new chemical compounds on cancer cells, the change in cell shape over time provides vital information about cell health. Live-cell image analysis systems can be used to extract cell-shape describing parameters of individual cells during exposure to new drugs. Multivariate statistical analysis is then applied to understand cell morphology and the correlation between various shape descriptors. Principal component analysis integrated with histogram distribution analysis is a method to compress and summarize important cellular data features without loss of information about the individual cell shapes. A workflow for this kind of analysis is being developed at Sartorius and aims to aid in the biological interpretation of different experimental results. However, methods for exploring the time dimension in the experiments are not yet fully explored, and a temporal view of the data would increase understanding of the change in cell morphology metrics over time. In this study, we implement the workflow to a data set generated from the microscope IncuCyte and investigate a possible continuation of time-series analysis on the data. The results demonstrate how we can use principal component analysis in two steps together with histogram distributions of different experimental conditions to study cell shapes over time. Scores and loadings from the analysis are used as new observations representing the original data, and the evolution of score-value can be backtracked to cell morphology metrics changing in time. The results show a comprehensive way of studying how cells from all experimental conditions relate to each other during the course of an experiment. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182264application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Mathematical Analysis
Matematisk analys
spellingShingle Mathematical Analysis
Matematisk analys
Krantz, Amanda
Temporal Multivariate Distribution Analysis of Cell Shape Descriptors
description In early drug discovery and the study of the effects of new chemical compounds on cancer cells, the change in cell shape over time provides vital information about cell health. Live-cell image analysis systems can be used to extract cell-shape describing parameters of individual cells during exposure to new drugs. Multivariate statistical analysis is then applied to understand cell morphology and the correlation between various shape descriptors. Principal component analysis integrated with histogram distribution analysis is a method to compress and summarize important cellular data features without loss of information about the individual cell shapes. A workflow for this kind of analysis is being developed at Sartorius and aims to aid in the biological interpretation of different experimental results. However, methods for exploring the time dimension in the experiments are not yet fully explored, and a temporal view of the data would increase understanding of the change in cell morphology metrics over time. In this study, we implement the workflow to a data set generated from the microscope IncuCyte and investigate a possible continuation of time-series analysis on the data. The results demonstrate how we can use principal component analysis in two steps together with histogram distributions of different experimental conditions to study cell shapes over time. Scores and loadings from the analysis are used as new observations representing the original data, and the evolution of score-value can be backtracked to cell morphology metrics changing in time. The results show a comprehensive way of studying how cells from all experimental conditions relate to each other during the course of an experiment.
author Krantz, Amanda
author_facet Krantz, Amanda
author_sort Krantz, Amanda
title Temporal Multivariate Distribution Analysis of Cell Shape Descriptors
title_short Temporal Multivariate Distribution Analysis of Cell Shape Descriptors
title_full Temporal Multivariate Distribution Analysis of Cell Shape Descriptors
title_fullStr Temporal Multivariate Distribution Analysis of Cell Shape Descriptors
title_full_unstemmed Temporal Multivariate Distribution Analysis of Cell Shape Descriptors
title_sort temporal multivariate distribution analysis of cell shape descriptors
publisher Umeå universitet, Institutionen för fysik
publishDate 2021
url http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182264
work_keys_str_mv AT krantzamanda temporalmultivariatedistributionanalysisofcellshapedescriptors
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