Global coordination level in single-cell transcriptomic data
Abstract Genes are linked by underlying regulatory mechanisms and by jointly implementing biological functions, working in coordination to apply different tasks in the cells. Assessing the coordination level between genes from single-cell transcriptomic data, without a priori knowledge of the map of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-11507-y |
| _version_ | 1818211368627601408 |
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| author | Guy Amit Dana Vaknin Ben Porath Orr Levy Omer Hamdi Amir Bashan |
| author_facet | Guy Amit Dana Vaknin Ben Porath Orr Levy Omer Hamdi Amir Bashan |
| author_sort | Guy Amit |
| collection | DOAJ |
| description | Abstract Genes are linked by underlying regulatory mechanisms and by jointly implementing biological functions, working in coordination to apply different tasks in the cells. Assessing the coordination level between genes from single-cell transcriptomic data, without a priori knowledge of the map of gene regulatory interactions, is a challenge. A ‘top-down’ approach has recently been developed to analyze single-cell transcriptomic data by evaluating the global coordination level between genes (called GCL). Here, we systematically analyze the performance of the GCL in typical scenarios of single-cell RNA sequencing (scRNA-seq) data. We show that an individual anomalous cell can have a disproportionate effect on the GCL calculated over a cohort of cells. In addition, we demonstrate how the GCL is affected by the presence of clusters, which are very common in scRNA-seq data. Finally, we analyze the effect of the sampling size of the Jackknife procedure on the GCL statistics. The manuscript is accompanied by a description of a custom-built Python package for calculating the GCL. These results provide practical guidelines for properly pre-processing and applying the GCL measure in transcriptional data. |
| first_indexed | 2024-12-12T05:31:24Z |
| format | Article |
| id | doaj.art-3d76226710254bb48813898e3cae031c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T05:31:24Z |
| publishDate | 2022-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3d76226710254bb48813898e3cae031c2022-12-22T00:36:18ZengNature PortfolioScientific Reports2045-23222022-05-0112111110.1038/s41598-022-11507-yGlobal coordination level in single-cell transcriptomic dataGuy Amit0Dana Vaknin Ben Porath1Orr Levy2Omer Hamdi3Amir Bashan4Physics Department, Bar-Ilan UniversityPhysics Department, Bar-Ilan UniversityDepartment of Immunobiology, Howard Hughes Medical Institute, Yale University School of MedicinePhysics Department, Bar-Ilan UniversityPhysics Department, Bar-Ilan UniversityAbstract Genes are linked by underlying regulatory mechanisms and by jointly implementing biological functions, working in coordination to apply different tasks in the cells. Assessing the coordination level between genes from single-cell transcriptomic data, without a priori knowledge of the map of gene regulatory interactions, is a challenge. A ‘top-down’ approach has recently been developed to analyze single-cell transcriptomic data by evaluating the global coordination level between genes (called GCL). Here, we systematically analyze the performance of the GCL in typical scenarios of single-cell RNA sequencing (scRNA-seq) data. We show that an individual anomalous cell can have a disproportionate effect on the GCL calculated over a cohort of cells. In addition, we demonstrate how the GCL is affected by the presence of clusters, which are very common in scRNA-seq data. Finally, we analyze the effect of the sampling size of the Jackknife procedure on the GCL statistics. The manuscript is accompanied by a description of a custom-built Python package for calculating the GCL. These results provide practical guidelines for properly pre-processing and applying the GCL measure in transcriptional data.https://doi.org/10.1038/s41598-022-11507-y |
| spellingShingle | Guy Amit Dana Vaknin Ben Porath Orr Levy Omer Hamdi Amir Bashan Global coordination level in single-cell transcriptomic data Scientific Reports |
| title | Global coordination level in single-cell transcriptomic data |
| title_full | Global coordination level in single-cell transcriptomic data |
| title_fullStr | Global coordination level in single-cell transcriptomic data |
| title_full_unstemmed | Global coordination level in single-cell transcriptomic data |
| title_short | Global coordination level in single-cell transcriptomic data |
| title_sort | global coordination level in single cell transcriptomic data |
| url | https://doi.org/10.1038/s41598-022-11507-y |
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