A schematic showcasing the methodology that computes the community stability for a Hi-C network.
<p>Using the original Hi-C data, we bootstrap by sampling data from the contact distributions associated with each distance (<a href="https://journals.plos.org/complexsystems//article/info:doi/10.1371/journal.pcsy.0000053#pcsy.0000053.g002" target="_blank">Fig 2</a...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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إضافة وسم
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| الملخص: | <p>Using the original Hi-C data, we bootstrap by sampling data from the contact distributions associated with each distance (<a href="https://journals.plos.org/complexsystems//article/info:doi/10.1371/journal.pcsy.0000053#pcsy.0000053.g002" target="_blank">Fig 2</a>). We then run a community detection algorithm on the original Hi-C data to get the original partition (GenLouvain). To reduce the number of samples required for good statistics, we use the original partition as an initial partition for GenLouvain. Each sample gives a list of partitions, each with unique communities, shown as boxes along the diagonal colored by the community number ID. Based on the overlap (calculated with the Jaccard index), we count the number of times each community in the original Hi-C data appears in the bootstrapped data, which we use as a proxy for stability. (rightmost panel) Original partition, where a darker color indicates communities with higher stability.</p> |
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