TCGAvisualize_SurvivalCoxNET {TCGAbiolinks} | R Documentation

## Survival analysis with univariate Cox regression package (dnet)

### Description

TCGAvisualize_SurvivalCoxNET can help an user to identify a group of survival genes that are significant from univariate Kaplan Meier Analysis and also for Cox Regression. It shows in the end a network build with community of genes with similar range of pvalues from Cox regression (same color) and that interaction among those genes is already validated in literatures using the STRING database (version 9.1). TCGAvisualize_SurvivalCoxNET perform survival analysis with univariate Cox regression and package (dnet) using following functions wrapping from these packages:

1. survival::coxph

2. igraph::subgraph.edges

3. igraph::layout.fruchterman.reingold

4. igraph::spinglass.community

5. igraph::communities

6. dnet::dRDataLoader

7. dnet::dNetInduce

8. dnet::dNetPipeline

9. dnet::visNet

10. dnet::dCommSignif

### Usage

    TCGAvisualize_SurvivalCoxNET(
      clinical_patient,
      dataGE,
      Genelist,
      org.Hs.string,
      scoreConfidence = 700,
      titlePlot = "TCGAvisualize_SurvivalCoxNET Example"
    )

### Arguments

`clinical_patient` | is a data.frame using function 'clinic' with information related to barcode / samples such as bcr_patient_barcode, days_to_death , days_to_last_followup , vital_status, etc
`dataGE` | is a matrix of Gene expression (genes in rows, samples in cols) from TCGAprepare
`Genelist` | is a list of gene symbols where perform survival KM.
`org.Hs.string` | an igraph object that contains a functional protein association network in human. The network is extracted from the STRING database (version 10).
`scoreConfidence` | restrict to those edges with high confidence (eg. score>=700)
`titlePlot` | is the title to show in the final plot.

### Details

TCGAvisualize_SurvivalCoxNET allow user to perform the complete workflow using coxph and dnet package related to survival analysis with an identification of gene-active networks from high-throughput omics data using gene expression and clinical data.

1. Cox regression survival analysis to obtain hazard ratio (HR) and p-values

2. fit a Cox proportional hazards model and ANOVA (Chisq test)

3. Network comunites

4. An igraph object that contains a functional protein association network in human. The network is extracted from the STRING database (version 9.1). Only those associations with medium confidence (score>=400) are retained.

5. restrict to those edges with high confidence (score>=700)

6. extract network that only contains genes in pvals

7. Identification of gene-active network

8. visualisation of the gene-active network itself

9. the layout of the network visualisation (fixed in different visuals)

10. color nodes according to communities (identified via a spin-glass model and simulated annealing)

11. node sizes according to degrees

12. highlight different communities

13. visualize the subnetwork

### Value

net IGRAPH with related Cox survival genes in community (same pval and color) and with interactions from STRING database.

[Package TCGAbiolinks version 2.18.0 Index]