TCGA-READ: analysis of the methylation alteration and survival rate.
This tutorial is composed of 4 parts.
Part1 '00"00 - A Preparation.
We previously extracted hypo- or hyper-methylated sites from 5 Normal-Tumor pairs. So we import the analyzed data, and also all READ tumor samples. The point is merging platforms before going to the next step.
Part2 '02"50 - Setting Up A Series of All Tumor Samples.
We setup the series to make meaningful visualizations. And we use TCGA sample attributes information to see the effect of those parameters on the survival rate.
Part3 '06"20 - A Clustering Analysis of Hypo- or Hyper-Methylated Sites.
Part4 '10"15 - Kaplan Meier Survival Curve Analysis
We use Kaplan-Meier Survival Curve tool to see the effect of the parameters. Interestingly, it shows hypo-methylated sites do not have clear effect though, hyper-methylated sites may be able to separate samples into good and bad outcomes.
Please take an Online Training for a full instruction of the data analysis.
After this analysis, you may want to see the expression profiles of those genes. You can see the RNA-Seq data to examine the effect of expression profiles and survival rate .
Download SSA Files
- TCGA READ RNA-Seq (matched with methylation data).ssa (12.8MB)
- TCGA READ RNA-Seq Tumor samples.ssa (159MB)
methylation array data
Analyzing a large data set requires large memory (RAM) on the computer. If memory error often happens, you need to add RAM physically though, there are workarounds to handle with a limited memory.