Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Clustering time-course data using P-splines and mixed effects mixture models
Mini Dissertation (MCom (Advanced Data Analytics))--University of Pretoria 2022.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
University of Pretoria
2022
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613473931788288 |
|---|---|
| access_status_str | Open Access |
| author2 | Kanfer, F.H.J. (Frans) |
| author_browse | Kanfer, F.H.J. (Frans) |
| author_facet | Kanfer, F.H.J. (Frans) |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MCom (Advanced Data Analytics))--University of Pretoria 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/83444 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:42.914Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/83444 Clustering time-course data using P-splines and mixed effects mixture models Kanfer, F.H.J. (Frans) U04639864@tuks.co.za Millard, Sollie M. Bredenkamp, Deidre UCTD Statistics Mini Dissertation (MCom (Advanced Data Analytics))--University of Pretoria 2022. In the field of biology, gene expressions are evaluated over time to study complicated biological processes and genetic supervisory networks. Because the process is continuous, time-course gene-expression data may be represented by a continuous function. This mini dissertation addresses cluster analysis of time-course data in a mixture model framework. To take into account the time dependency of such time-course data, as well as the degree of error present in many datasets, the mixed effects model with penalized B-splines is considered. In this mini dissertation the performance of such a mixed effects model has been studied with regards to the clustering of time-course gene expression data in a mixture model system. The EM algorithm has been implemented to fit the mixture model in a mixed effects model structure. For each subject the best linear unbiased smooth estimate of its time-course trajectory has been calculated and subjects with similar mean curves have been clustered in the same cluster. Model validation statistics such has the model accuracy and the coefficient of determination (R 2 ) indicates that the model can cluster simulated data effectively into clusters that differ in either the form of the curves or the timing to the curves’ peaks. The proposed technique is further evidenced by clustering time-course gene expression data consisting of microarray samples from lung tissue of mice exposed to different Influenza strains from 14 time-points. National Research Foundation, South Africa (Research chair: Computational and Methodological Statistics, Grant number 71199)(SARChI). Statistics MCom (Advanced Data Analytics) Unrestricted 2022-01-25T07:43:09Z 2022-01-25T07:43:09Z 2022-08 2022 Mini Dissertation Bredenkamp, DM 2022, Clustering time-course data using P-splines and mixed effects mixture models, MSc Mini Dissertation, University of Pretoria, Pretoria viewed yymmdd http://hdl.handle.net/2263/83444 A2022 http://hdl.handle.net/2263/83444 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Statistics Clustering time-course data using P-splines and mixed effects mixture models |
| title | Clustering time-course data using P-splines and mixed effects mixture models |
| title_full | Clustering time-course data using P-splines and mixed effects mixture models |
| title_fullStr | Clustering time-course data using P-splines and mixed effects mixture models |
| title_full_unstemmed | Clustering time-course data using P-splines and mixed effects mixture models |
| title_short | Clustering time-course data using P-splines and mixed effects mixture models |
| title_sort | clustering time course data using p splines and mixed effects mixture models |
| topic | UCTD Statistics |
| url | http://hdl.handle.net/2263/83444 |