WFCCM Pre-Post Guide

This is a guide for running pre-post analysis in WFCCM.

For a pre-post analysis, there needs to be a pre-treatment and post-treatment sample for each patient. Missing the pre or post sample will result in that patient being omitted.

NOTE: This is a possibility for a new feature in WFCCM (similar to Average by patient).

Combining data

Until General Wfccm Design incorporates this process, you'll have to use your favorite spreadsheet. First, all patients need a unique identifier. It helps to specify pre/post by a suffix. For example, the columns p1-pre and p1-post would represent the pre and post-treatment samples for p1. As good measure, I would call the pre-post column p1.

Generally, percent change is calculated by post/pre - 1. Given the nature of MicroArray and MassSpec data, there will be cases when this formula will not suffice.

post = 0 post <> 0
pre = 0 DONE DONE
pre <> 0 DONE  

Log N

Calculating percent change after the log transformation will give different results than before (especially when pre = post). For that reason, we want to calculate percent change on the log-transformed data. Normalized data will suffice in cases such as using Shuo's wavelet preprocessing. In this case, percent change is log(post)/log(pre).

This percent change data will be used for the following methods:
  • TTest
  • Wilcoxon
  • KS
  • SAM
  • WGA
  • InfoScore
  • HuWright

post = 0 post <> 0
pre = 0 . (NaN) ?
pre <> 0 ? log(post)/log(pre)

Binary

With zero (or missing) values, the percent change does not make sense anymore. Instead, calculate a binary value to run Fisher.

post = 0 post <> 0
pre = 0 . (NaN) 1
pre <> 0 0 ?

Scores and Distance

Run the appropriates scores for the log and binary datasets, then merge. Choose your model and run distance as normal with the log dataset.
Topic revision: r2 - 18 May 2005, WillGray
 

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