![](\"https://s3.amazonaws.com/4dn-dcic-public/static-pages/ISC_analysis_plots/ISC_and_compartments_human_correlations_distribution_plot_v2.png\"/)
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\nThe datasets were divided into six categories:\n\n**Low Cis/Trans Ratio:** Datasets that have either a cis/trans ratio less than 50 or less than 40 for H1 cell lines.\n\n**CTCF & Cohesion Depletion:** Datasets where CTCF or Cohesin were depleted.\n\n**Cohesion Release Reduction:** Datasets where Cohesin release factors (WALP & PDS5A/B) were depleted. \n\n**Protocol Variations:** Datasets produced by major variations to the standard Hi-C protocol, such as skipping the ligation step.\n\n** Cell Cycle Studies:** Datasets where different phases of the cell cycle were studied.\n\n**Regular:** Datasets that do not fall in any of the categories above.\n\nThe table used to generate the plot above can be downloaded here.", "name": "resources.data-analysis.correlation_analysis_page", "award": {"@id": "/awards/1U01CA200059-01/", "project": "4DN", "@type": ["Award", "Item"], "name": "1U01CA200059-01", "display_title": "4D NUCLEOME NETWORK DATA COORDINATION AND INTEGRATION CENTER - PHASE I", "uuid": "b0b9c607-f8b4-4f02-93f4-9895b461334b", "description": "DCIC: The goals of the 4D Nucleome (4DN) Data Coordination and Integration Center (DCIC) are to collect, store, curate, display, and analyze data generated in the 4DN Network. We have assembled a team of investigators with a strong track record in analysis of chromatin interaction data, image processing and three-dimensional data visualization, integrative analysis of genomic and epigenomic data, data portal development, large-scale computing, and development of secure and flexible cloud technologies. In Aim 1, we will develop efficient submission pipelines for data and metadata from 4DN data production groups. We will define data/metadata requirements and quality metrics in conjunction with the production groups and ensure that high-quality, well- annotated data become available to the wider scientific community in a timely manner. In Aim 2, we will develop a user-friendly data portal for the broad scientific community. This portal will provide an easy-to-navigate interface for accessing raw and intermediate data files, allow for programmatic access via APIs, and will incorporate novel analysis and visualization tools developed by DCIC as well as other Network members. For computing and storage scalability and cost-effectiveness, significant efforts will be devoted to development and deployment of cloud-based technology. We will conduct tutorials and workshops to facilitate the use of 4DN data and tools by external investigators. In Aim 3, we will coordinate and assist in conducting integrative analysis of the multiple data types. These efforts will examine key questions in higher-order chromatin organization using both sequence and image data, and the tools and algorithms developed here will be incorporated into the data portal for use by other investigators. These three aims will ensure that the data generated in 4DN will have maximal impact for the scientific community.", "status": "current", "center_title": "DCIC - DCIC", "pi": {"error": "no view permissions"}, "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin"]}}, "title": "Comparison Across Cell Types", "status": "released", "aliases": ["4dn-dcic-lab:resources.data-analysis.correlations_analysis_page"], "options": {"filetype": "md", "collapsible": true, "default_open": true}, "date_created": "2020-12-05T01:10:40.277828+00:00", "section_type": "Page Section", "submitted_by": {"error": "no view permissions"}, "last_modified": {"modified_by": {"error": "no view permissions"}, "date_modified": "2021-02-18T15:01:06.473299+00:00"}, "schema_version": "2", "@id": "/static-sections/1cf3f333-317e-4a4a-b618-1dd542ad09b0/", "@type": ["StaticSection", "UserContent", "Item"], "uuid": "1cf3f333-317e-4a4a-b618-1dd542ad09b0", "principals_allowed": {"view": ["system.Everyone"], "edit": ["group.admin", "role.owner", "userid.56c9c683-bb11-471b-b590-c656f7dc03c1"]}, "display_title": "Comparison Across Cell Types", "external_references": [], "content": "\nWhile insulation and compartment score tracks show cell-to-cell variability, interphase cells of various cell types share a significant portion of their chromosome conformations, due to the presence of constitutive heterochromatin, housekeeping genes, and other constitutive features. Therefore, genome-wide correlation for these tracks between cell types can constitute a quality control on the score calculations. \n\nAfter the pipelines were run on the released data in the data portal (insulation scores were only run on mcool files with over 100M reads, see above), Spearman correlations were calculated relative to reference tracks. The deep Micro-C datasets for H1-ESC (4DNES21D8SP8) and HFFc6 (4DNESWST3UBH) were used as reference for insulation scores and compartments respectively. \nBelow is the distribution of those correlations.\n\n
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\nThe datasets were divided into six categories:\n\n**Low Cis/Trans Ratio:** Datasets that have either a cis/trans ratio less than 50 or less than 40 for H1 cell lines.\n\n**CTCF & Cohesion Depletion:** Datasets where CTCF or Cohesin were depleted.\n\n**Cohesion Release Reduction:** Datasets where Cohesin release factors (WALP & PDS5A/B) were depleted. \n\n**Protocol Variations:** Datasets produced by major variations to the standard Hi-C protocol, such as skipping the ligation step.\n\n** Cell Cycle Studies:** Datasets where different phases of the cell cycle were studied.\n\n**Regular:** Datasets that do not fall in any of the categories above.\n\nThe table used to generate the plot above can be downloaded here.", "filetype": "md", "content_as_html": "
While insulation and compartment score tracks show cell-to-cell variability, interphase cells of various cell types share a significant portion of their chromosome conformations, due to the presence of constitutive heterochromatin, housekeeping genes, and other constitutive features. Therefore, genome-wide correlation for these tracks between cell types can constitute a quality control on the score calculations.
\nAfter the pipelines were run on the released data in the data portal (insulation scores were only run on mcool files with over 100M reads, see above), Spearman correlations were calculated relative to reference tracks. The deep Micro-C datasets for H1-ESC (4DNES21D8SP8) and HFFc6 (4DNESWST3UBH) were used as reference for insulation scores and compartments respectively. \nBelow is the distribution of those correlations.
\n![](\"https://s3.amazonaws.com/4dn-dcic-public/static-pages/ISC_analysis_plots/ISC_and_compartments_human_correlations_distribution_plot_v2.png\")
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\nThe datasets were divided into six categories:\n\n**Low Cis/Trans Ratio:** Datasets that have either a cis/trans ratio less than 50 or less than 40 for H1 cell lines.\n\n**CTCF & Cohesion Depletion:** Datasets where CTCF or Cohesin were depleted.\n\n**Cohesion Release Reduction:** Datasets where Cohesin release factors (WALP & PDS5A/B) were depleted. \n\n**Protocol Variations:** Datasets produced by major variations to the standard Hi-C protocol, such as skipping the ligation step.\n\n** Cell Cycle Studies:** Datasets where different phases of the cell cycle were studied.\n\n**Regular:** Datasets that do not fall in any of the categories above.\n\nThe table used to generate the plot above can be downloaded here.