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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Online Tables for the thesis "Clustering approaches for patient stratification in psychiatry" by Jonas Hagenberg

Authors: Hagenberg, Jonas;

Online Tables for the thesis "Clustering approaches for patient stratification in psychiatry" by Jonas Hagenberg

Abstract

This repository contains the online tables that accompany my thesis "Clustering approaches for patient stratification in psychiatry". In the following, I list the description of all tables: 1: Information about somatic diseases and medication separated by status (participants labeled as cases and used in the clustering as well as controls without a DSM-IV diagnosis). N data denotes the number of individuals who had the information available. The medication information was not available for the OPTIMA cohort. N denotes the individuals who were affected by the disease or took the medication. 2: Missingness and coefficient of variation information of the immune marker data for 237 participants used in the clustering and 36 controls without a DSM-IV diagnosis. The coefficient of variation was calculated from three internal controls that were run in duplicates. 3: Variable importance of all variables the initial clustering. The variable importance is calculated as the F-value from an ANOVA model with the clusters as independent variables. The variable importance cannot be interpreted as a p-value as the variables were already used in the clustering, thereby inflating the p-values. Full version of supplementary table 3. 4: Variable importance of all gene sets in the initial clustering. Full version of supplementary table 8. 5: Variable importance of all variables the secondary analysis corrected for age, sex and BMI. Full version of supplementary table 9. 6: Variable importance of all variables the exploratory analysis including cell type proportions. Full version of supplementary table 10. 7: Variable importance of all gene sets in the exploratory analysis including cell type proportions. Full version of supplementary table 11. 8: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 12. 9: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP, IL-6 and BMI. Full version of supplementary table 13. 10: Enriched hallmark gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. 11: Enriched hallmark gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. 12: Enriched hallmark gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. 13: Enriched GO biological pathway gene sets with regard to CRP calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. Full version of supplementary table 15. 14: Enriched GO biological pathway gene sets with regard to IL-6 calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. 15: Enriched GO biological pathway gene sets with regard to BMI calculated with the results from the model containing CRP, IL-6 and BMI separated by cell type. 16: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6. 17: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and IL-6. 18: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI. 19: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained CRP and BMI. 20: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI. 21: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained IL-6 and BMI. 22: Differentially expressed genes with regard to the CRP concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only CRP. 23: Differentially expressed genes with regard to the IL-6 concentration separated by cell type. The analysis was performed with DESeq2 and the model contained only IL-6. 24: Differentially expressed genes with regard to BMI separated by cell type. The analysis was performed with DESeq2 and the model contained only BMI. 25: Variable importance of initial multi-omics clustering of the DEGs identified in the single cell data set.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average