Identification of hub DE-SRGs.
<p><b>A.</b> Volcano plot of DEGs, with gene symbols of hub DE-SRGs labeled and red lines illustrating the PPI between them. The size of each point indicates the gene’s importance within the PPI network. <b>B.</b> GSEA analysis demonstrating senescence-related pathways...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1852024869513854976 |
|---|---|
| author | Xihao Shen (20347942) |
| author2 | Jiyue Wu (3814717) Feilong Zhang (420300) Qing Bi (3864823) Zejia Sun (10031646) Wei Wang (17594) |
| author2_role | author author author author author |
| author_facet | Xihao Shen (20347942) Jiyue Wu (3814717) Feilong Zhang (420300) Qing Bi (3864823) Zejia Sun (10031646) Wei Wang (17594) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xihao Shen (20347942) Jiyue Wu (3814717) Feilong Zhang (420300) Qing Bi (3864823) Zejia Sun (10031646) Wei Wang (17594) |
| dc.date.none.fl_str_mv | 2024-11-27T18:29:39Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0312272.g002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Identification_of_hub_DE-SRGs_/27920716 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology Genetics Molecular Biology Neuroscience Biotechnology Developmental Biology Marine Biology Cancer Virology Computational Biology weighted gene co two clusters underscored multiple established databases machine learning algorithms integrative machine learning gene expression omnibus demographic shift towards consensus clustering algorithm b >(</ b aged donor kidneys term graft outcomes expression network analysis differential expression analysis comprehensive analysis underscores worse graft survival reduced graft survival predicting graft survival two rejection clusters rejection remains elusive optimal acute rejection delayed graft function ktx rejection poses ktx rejection occurrence identify predictive srgs invasive diagnostic model kidney transplant rejection graft survival ktx rejection kidney graft diagnostic model kidney rejection gsva analysis cluster analysis allograft function rejection samples diagnosing rejection prognostic model kidney transplantation xlink "> wgcna ), significant threat related genes provided microarray positive correlation level landscape ischemic damage detailed cellular conducted using conclusions drawn cluster c1 c2 ). |
| dc.title.none.fl_str_mv | Identification of hub DE-SRGs. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p><b>A.</b> Volcano plot of DEGs, with gene symbols of hub DE-SRGs labeled and red lines illustrating the PPI between them. The size of each point indicates the gene’s importance within the PPI network. <b>B.</b> GSEA analysis demonstrating senescence-related pathways differently enriched between normal and rejection samples. <b>C.</b> Correlation heatmaps between different modules and rejection via WGCNA analysis. <b>D.</b> Correlation analysis of the module brown displaying the module connectivity of genes on the x-axis against the correlation coefficient with the phenotype on the y-axis. <b>E.</b> Intersection of DEGs, SRGs and genes from the brown module yielding 33 hub DE-SRGs. <b>F.</b> Heatmap of 33 hub DE-SRGs expression profiles in normal and rejection samples.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_fbf9d78739a54eaf0dfd40eeed781e2f |
| identifier_str_mv | 10.1371/journal.pone.0312272.g002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27920716 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Identification of hub DE-SRGs.Xihao Shen (20347942)Jiyue Wu (3814717)Feilong Zhang (420300)Qing Bi (3864823)Zejia Sun (10031646)Wei Wang (17594)Cell BiologyGeneticsMolecular BiologyNeuroscienceBiotechnologyDevelopmental BiologyMarine BiologyCancerVirologyComputational Biologyweighted gene cotwo clusters underscoredmultiple established databasesmachine learning algorithmsintegrative machine learninggene expression omnibusdemographic shift towardsconsensus clustering algorithmb >(</ baged donor kidneysterm graft outcomesexpression network analysisdifferential expression analysiscomprehensive analysis underscoresworse graft survivalreduced graft survivalpredicting graft survivaltwo rejection clustersrejection remains elusiveoptimal acute rejectiondelayed graft functionktx rejection posesktx rejection occurrenceidentify predictive srgsinvasive diagnostic modelkidney transplant rejectiongraft survivalktx rejectionkidney graftdiagnostic modelkidney rejectiongsva analysiscluster analysisallograft functionrejection samplesdiagnosing rejectionprognostic modelkidney transplantationxlink ">wgcna ),significant threatrelated genesprovided microarraypositive correlationlevel landscapeischemic damagedetailed cellularconducted usingconclusions drawncluster c1c2 ).<p><b>A.</b> Volcano plot of DEGs, with gene symbols of hub DE-SRGs labeled and red lines illustrating the PPI between them. The size of each point indicates the gene’s importance within the PPI network. <b>B.</b> GSEA analysis demonstrating senescence-related pathways differently enriched between normal and rejection samples. <b>C.</b> Correlation heatmaps between different modules and rejection via WGCNA analysis. <b>D.</b> Correlation analysis of the module brown displaying the module connectivity of genes on the x-axis against the correlation coefficient with the phenotype on the y-axis. <b>E.</b> Intersection of DEGs, SRGs and genes from the brown module yielding 33 hub DE-SRGs. <b>F.</b> Heatmap of 33 hub DE-SRGs expression profiles in normal and rejection samples.</p>2024-11-27T18:29:39ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0312272.g002https://figshare.com/articles/figure/Identification_of_hub_DE-SRGs_/27920716CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/279207162024-11-27T18:29:39Z |
| spellingShingle | Identification of hub DE-SRGs. Xihao Shen (20347942) Cell Biology Genetics Molecular Biology Neuroscience Biotechnology Developmental Biology Marine Biology Cancer Virology Computational Biology weighted gene co two clusters underscored multiple established databases machine learning algorithms integrative machine learning gene expression omnibus demographic shift towards consensus clustering algorithm b >(</ b aged donor kidneys term graft outcomes expression network analysis differential expression analysis comprehensive analysis underscores worse graft survival reduced graft survival predicting graft survival two rejection clusters rejection remains elusive optimal acute rejection delayed graft function ktx rejection poses ktx rejection occurrence identify predictive srgs invasive diagnostic model kidney transplant rejection graft survival ktx rejection kidney graft diagnostic model kidney rejection gsva analysis cluster analysis allograft function rejection samples diagnosing rejection prognostic model kidney transplantation xlink "> wgcna ), significant threat related genes provided microarray positive correlation level landscape ischemic damage detailed cellular conducted using conclusions drawn cluster c1 c2 ). |
| status_str | publishedVersion |
| title | Identification of hub DE-SRGs. |
| title_full | Identification of hub DE-SRGs. |
| title_fullStr | Identification of hub DE-SRGs. |
| title_full_unstemmed | Identification of hub DE-SRGs. |
| title_short | Identification of hub DE-SRGs. |
| title_sort | Identification of hub DE-SRGs. |
| topic | Cell Biology Genetics Molecular Biology Neuroscience Biotechnology Developmental Biology Marine Biology Cancer Virology Computational Biology weighted gene co two clusters underscored multiple established databases machine learning algorithms integrative machine learning gene expression omnibus demographic shift towards consensus clustering algorithm b >(</ b aged donor kidneys term graft outcomes expression network analysis differential expression analysis comprehensive analysis underscores worse graft survival reduced graft survival predicting graft survival two rejection clusters rejection remains elusive optimal acute rejection delayed graft function ktx rejection poses ktx rejection occurrence identify predictive srgs invasive diagnostic model kidney transplant rejection graft survival ktx rejection kidney graft diagnostic model kidney rejection gsva analysis cluster analysis allograft function rejection samples diagnosing rejection prognostic model kidney transplantation xlink "> wgcna ), significant threat related genes provided microarray positive correlation level landscape ischemic damage detailed cellular conducted using conclusions drawn cluster c1 c2 ). |