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- % Frozen RMA implementation for TGI PAX and BX samples
- % Ryan Thompson
- % Thu Mar 19, 2015
- # Results #
- The scripts below were used to evaluate the consistency of the fRMA
- normalization vectors by repeating the training process with 5
- different random samples and then comparing a random selection of
- arrays normalized by all five trained vectors as well as by ordinary
- RMA. [This folder](fRMA_consistency_results) shows the results.
- # Scripts #
- There are two pairs of scripts. The first pair, `train.R` and
- `test.R`, handle the tasks of (respectively) generating/training the
- main fRMA vectors and ensuring that they work by normalizing all the
- data with them. The second pair, `consistency-train.R` and
- `consistency-evaluate.R`, handle (respectively) training five separate
- fRMA vector sets and testing their consistency.
- ## [`train.R`](train.R): Creating the fRMA vectors ##
- This script reads the sample metadata tables, assembles the full file
- lists for BX and PAX tissues, and trains a set of fRMA vectors for
- each tissue. It exports each of these vector sets to an installable R
- package.
- ## [`test.R`](test.R): Testing the fRMA vectors ##
- This script simply loads all the arrays and normalizes them using the
- appropriate fRMA vectors that were generated by `train.R`. It should
- be run after installing the packages produced by `train.R`. It is
- simply used for testing to make sure the fRMA vectors work.
- ## [`consistency-train.R`](consistency-train.R): Train several vector sets for each tissue ##
- This script essentially does the same thing as `train.R`, only it does
- it five times with five different subsamplings of the arrays to
- generate five different fRMA vector sets and saves them all in an R
- data file.
- ## [`consistency-evaluate.R`](consistency-evaluate.R): Verify consistency of fRMA vectors ##
- This script loads the data file from `consistency-train.R`, then loads
- 20 random arrays from each tissue and normalizes them with all five
- fRMA vector sets, and also by ordinary RMA. It then produces plots of
- M vs A for every pair of normalizations. Unlike regular MA plots,
- these are *not* plotting arrays against each other, but rather arrays
- against themselves, but normalized using two different methods. So if
- two normalizations were perfectly consistent, the MA plot would be a
- flat horizontal line at M=0. It also produces boxplots and violin
- plots showing the M distribution for each of the pairwise comparisons.
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