Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays.
Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010505 |
| _version_ | 1797777457957306368 |
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| author | Luke Ternes Jia-Ren Lin Yu-An Chen Joe W Gray Young Hwan Chang |
| author_facet | Luke Ternes Jia-Ren Lin Yu-An Chen Joe W Gray Young Hwan Chang |
| author_sort | Luke Ternes |
| collection | DOAJ |
| description | Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss, and long imaging acquisition time. As such, selecting which markers to include on a panel is important, since removing important markers will result in a loss of biologically relevant information, but identifying redundant markers will provide a room for other markers. To address this, we propose computational approaches to determine the amount of shared information between markers and select an optimally reduced panel that captures maximum amount of information with the fewest markers. Here we examine several panel selection approaches and evaluate them based on their ability to reconstruct the full panel images and information within breast cancer tissue microarray datasets using cyclic immunofluorescence as a proof of concept. We show that all methods perform adequately and can re-capture cell types using only 18 of 25 markers (72% of the original panel size). The correlation-based selection methods achieved the best single-cell marker mean intensity predictions with a Spearman correlation of 0.90 with the reduced panel. Using the proposed methods shown here, it is possible for researchers to design more efficient multiplex imaging panels that maximize the amount of information retained with the limited number of markers with respect to certain evaluation metrics and architecture biases. |
| first_indexed | 2024-03-12T23:04:13Z |
| format | Article |
| id | doaj.art-f0a9a6536ed14af9bc74d8fec6104081 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-03-12T23:04:13Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f0a9a6536ed14af9bc74d8fec61040812023-07-19T05:31:17ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101050510.1371/journal.pcbi.1010505Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays.Luke TernesJia-Ren LinYu-An ChenJoe W GrayYoung Hwan ChangRecent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss, and long imaging acquisition time. As such, selecting which markers to include on a panel is important, since removing important markers will result in a loss of biologically relevant information, but identifying redundant markers will provide a room for other markers. To address this, we propose computational approaches to determine the amount of shared information between markers and select an optimally reduced panel that captures maximum amount of information with the fewest markers. Here we examine several panel selection approaches and evaluate them based on their ability to reconstruct the full panel images and information within breast cancer tissue microarray datasets using cyclic immunofluorescence as a proof of concept. We show that all methods perform adequately and can re-capture cell types using only 18 of 25 markers (72% of the original panel size). The correlation-based selection methods achieved the best single-cell marker mean intensity predictions with a Spearman correlation of 0.90 with the reduced panel. Using the proposed methods shown here, it is possible for researchers to design more efficient multiplex imaging panels that maximize the amount of information retained with the limited number of markers with respect to certain evaluation metrics and architecture biases.https://doi.org/10.1371/journal.pcbi.1010505 |
| spellingShingle | Luke Ternes Jia-Ren Lin Yu-An Chen Joe W Gray Young Hwan Chang Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays. PLoS Computational Biology |
| title | Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays. |
| title_full | Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays. |
| title_fullStr | Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays. |
| title_full_unstemmed | Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays. |
| title_short | Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays. |
| title_sort | computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays |
| url | https://doi.org/10.1371/journal.pcbi.1010505 |
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