aCGH Survival Analysis
This is a statistical test (logrank) for survival data and called copy number data. The testing is recommended to be performed on high-dimensional data nodes containing chromosomal region information.
To begin the analysis, see Running the Analyses, then perform the following steps.
To perform an aCGH Survival Analysis:
Click the Advanced Workflow tab, then open the Analysis menu.
Select aCGH Survival Analysis.
The Variable Selection section appears.
Define the following variables:
- Region: A high-dimensional data node containing the chromosomal regions.
- Survival Time: A numerical data node containing survival time of interest (for example: Overall Survival [days]).
- Censoring Variable: A categorical data node indicating status subjects in which the selected event in survival time did NOT happen (for example, for Overall Survival Time, the Censoring Variable to select is Alive).
- Alteration type: The type of chromosomal alteration used to test association to survival (gains, losses, both).
- Permutations: The significance of the p-values is evaluated through permutations, and a false discovery rate is calculated. At least 10,000 permutations are recommended for final calculations. This will require a significant amount of time. (Permutations can be lowered for exploratory purposes.)
Click Run Analysis. As this may take a while, users are advised to select the option Run Job in Background in the popup window. The analysis can be retrieved at a later time in the Analysis Jobs tab.
Results appear in two sections:
- The chromosomal regions present in the high-dimensional data node are shown in a table, appended with p-values and false discovery rates.
- You can sort through the chromosomal regions, click on a region of interest and press the button Show Survival Plot. This will plot the survival curve for that particular region (see example below).
You can also opt to click Download Result, in which case both the table and all survival plots are obtained.
- Wiel et al. (2005) “CGHMultiArray: exact p-values for multi-array comparative genomic hybridization data.” Bioinformatics 21: 3193-3194.