In this TP, you will get some practice using the affyPLM package for assessing quality of Affymetrix GeneChips. As usual, you should always make sure you read the help documentation for each function you do not already know.
Here you will work through the affyPLM quality assessment vignette. Begin by starting R, then load the package and start the vignette:
library(affyPLM)
openVignette()
and select the number corresponding to Model Based QC Assessment of Affymetrix GeneChips. A window will open showing the pdf document.
Work through the vignette using the Dilution data.
Now you should carry out a quality assessment of the placenta chips (data from TP2). The code below assumes that your working directory contains the cel files:
plac <- ReadAffy()
Use fitPLM as above for quality assessment – look at the pseudo-images of the weights and of the positive and negative residuals to see what the outlier chip looks like compared to the others. You should also look at the NUSE and RLE boxplots.
Because one of the chips is an extreme outlier, we will exclude it from the analysis. Assuming that the index of the outlier is bad.chip, you can remove it as follows (bad.chip is either 1, 2, 3, 4, or 5; note we are also getting rid of the original plac here):
plac.new <- plac[,-bad.chip]
rm(plac)
Now convert the probe level measurements into a summary expression measure (RMA) and extract the expression values:
plac.rma <- rma(plac.new)
plac.exprs <- exprs(plac.rma)
The resulting expression values are an example of preprocessed data that could be analyzed. We won't do this now, but next week you will use limma to analyze an experiment done with Affymetrix GeneChips.
If you have time, visit the image gallery at http://plmimagegallery.bmbolstad.com/.
Scroll down to the bottom of the page and click on Assessment Plots for the Affymetrix U95A Spike-in Dataset. The NUSE plot indicates a few chips with high median NUSE. Go back and click on Image Gallery and have a look at the images for these chips. What do you think of their quality (compared to that of the other chips)?
An important part of the exam will be to carry out a quality assessment of the chips in your dataset and to explain the expression measure RMA. For exam practice, you can write a report on quality assessment and quantifying expression for the DAFL chips. You should include a small background on the experiment and chip summary, a description and summary of plots that you made for quality assessment, conclusions on chip quality, and a description of the expression summary (RMA). You should also include relevant plots, especially any plot which points out problems with a chip. This should not be more than 3 pages in total (less is better), and can be in English or French. Please send your report as a pdf file, and follow the naming convention: lastname3.pdf (e.g. my report would be goldstein3.pdf). You can email your report to me (darlene.goldstein at epfl.ch) by next Tuesday (any time).