Clinical data have individual differences and it often makes detecting changes difficult. If you can make pairs of samples from same patients, you can cancel individual difference and focus on effects of the parameter. Paired T-test cancels the variance in control samples, and takes only effect of the parameter. As a result, power of test improves by in general.
To apply paired T-test on Subio Platform, you need to define pairs and divide by control samples in each pair by setting of "Ratio to Control Samples" normalize block. And then apply "Compare to Control" tool of Basic Plug-in.
How to apply the paired T-test?
Clinical data have individual differences and it often makes detecting changes difficult.
If you can make pairs of samples from same patients, you can cancel individual difference and focus on effects of the parameter.
Please request a free Online Support, if you don't know how to do this.
In v1.20.5009 or later versions, you can much more easily define pairs in Ratio to Control Samples.
See the following movie from 24' 30".
Exploratory Analysis of TCGA-BLCA RNA Seq data
00:10 - The memory setting to handle large data sets.
00:40 - Importing samples from GDC data center.
01:30 - Creating a series, and set GDC sample annotations as parameters.
03:50 - Setting a normalization as viewing data distribution patterns.
06:20 - Filtering.
08:20 - PCA, and marking samples in a cluster.
08:50 - Visualizing parameters to help interpreting the result.
13:10 - Examining data distribution patterns of artificial effects.
18:20 - Excluding a part of samples from the analysis.
19:00 - Defining subgroups of tumor samples.
21:10 - Extracting differentially expressed genes between the subgroups.
22:00 - Creating a new series of Normal-Tumor paired samples.
24:30 - Making tumor/normal ratios to cancel individual differences.
27:00 - Examining "tumorization" effect on the expression profile.
27:30 - Defining 2 types of "tumorization" from a result of PCA.
29:10 - Extracting differentially expressed genes between the "tumorization" types.
30:00 - Comparing results for further analysis.
Please take an Online Training for a full instruction of the data analysis.