Deterministic patterns in single-cell transcriptomic data
Abstract We report the existence of deterministic patterns in statistical plots of single-cell transcriptomic data. We develop a theory showing that the patterns are neither artifacts introduced by the measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from...
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Nature Portfolio
2025-01-01
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Series: | npj Systems Biology and Applications |
Online Access: | https://doi.org/10.1038/s41540-025-00490-5 |
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author | Zhixing Cao Yiling Wang Ramon Grima |
author_facet | Zhixing Cao Yiling Wang Ramon Grima |
author_sort | Zhixing Cao |
collection | DOAJ |
description | Abstract We report the existence of deterministic patterns in statistical plots of single-cell transcriptomic data. We develop a theory showing that the patterns are neither artifacts introduced by the measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from finite sample size effects. The theory precisely predicts the patterns in data from multiplexed error-robust fluorescence in situ hybridization and five different types of single-cell sequencing platforms. |
format | Article |
id | doaj-art-985556d7e0e944ffb876898407158d45 |
institution | Kabale University |
issn | 2056-7189 |
language | English |
publishDate | 2025-01-01 |
publisher | Nature Portfolio |
record_format | Article |
series | npj Systems Biology and Applications |
spelling | doaj-art-985556d7e0e944ffb876898407158d452025-01-12T12:28:51ZengNature Portfolionpj Systems Biology and Applications2056-71892025-01-011111510.1038/s41540-025-00490-5Deterministic patterns in single-cell transcriptomic dataZhixing Cao0Yiling Wang1Ramon Grima2State Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologyState Key Laboratory of Bioreactor Engineering, East China University of Science and TechnologySchool of Biological Sciences, University of EdinburghAbstract We report the existence of deterministic patterns in statistical plots of single-cell transcriptomic data. We develop a theory showing that the patterns are neither artifacts introduced by the measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from finite sample size effects. The theory precisely predicts the patterns in data from multiplexed error-robust fluorescence in situ hybridization and five different types of single-cell sequencing platforms.https://doi.org/10.1038/s41540-025-00490-5 |
spellingShingle | Zhixing Cao Yiling Wang Ramon Grima Deterministic patterns in single-cell transcriptomic data npj Systems Biology and Applications |
title | Deterministic patterns in single-cell transcriptomic data |
title_full | Deterministic patterns in single-cell transcriptomic data |
title_fullStr | Deterministic patterns in single-cell transcriptomic data |
title_full_unstemmed | Deterministic patterns in single-cell transcriptomic data |
title_short | Deterministic patterns in single-cell transcriptomic data |
title_sort | deterministic patterns in single cell transcriptomic data |
url | https://doi.org/10.1038/s41540-025-00490-5 |
work_keys_str_mv | AT zhixingcao deterministicpatternsinsinglecelltranscriptomicdata AT yilingwang deterministicpatternsinsinglecelltranscriptomicdata AT ramongrima deterministicpatternsinsinglecelltranscriptomicdata |