Download affymetrix files from GEO
As seen above, several samples are reported 'outside bounds' by the RMA workflow. It means that some control probe sets did not meet the quality requirements. We looked it up and saw that the sample prep control probe sets targeting B.
Dap RNA is added in higher concentrations than thr RNA so the signal of dap should be higher than that of thr and this was not the case for the samples that were flagged 'outside bounds'.
The other control probes behaved as they should. So it might be that in some samples the reverse transcription of the high abundance transcripts was not completely efficient because of saturation As part of the standard Affymetrix microarray processing, control molecules are added to the mRNA at different concentrations prior to producing the cDNA.
Other molecules cDNA are added later in the sample preparation to control for hybridization on the chip. The out of bound errors reported above result from the discrepancy between the known spiked-in quantities and the readout after scanning the chip. The highest concentration of control does not produce a final value higher than a lower concentration of control which results in raising an alarm and showing the 4 samples with colored background.
Full details about the identity of the faulty probes and the obtained values can be found at the bottom table part of the full report linked in the next paragraph PDF. A number of QC plots can be generated using the right tools. Users are welcome to evaluate each QC plot by themselves using the data available on the server as input see link at the bottom of this page. Each group is in turn defined by moving CHP files to the appropriate group window. This is done for 'Heart' and for 'Diaphragm' samples.
Other expression analyses can be performed when the probe type is compatible with transcript level analysis discerning between alternative transcripts. However, this is not demonstrated here and we only provide the example of gene-level analysis. The filtering values can be adapted by the user to restrain or increase the DE gene list and new plots generated.
Additional graphs can be obtained to view the data from different angles. The scatter plot highlights potential differences between UR and DR genes between the groups.
The graphs are interactive and the user can query the full data to find which probesets or genes are UR or DR using the mouse and selecting area around points. Volcano plots are very popular and show how confident the data is and how many genes show deviation from the steady state.
The interactive nature of the plot allows identifying outliers or significantly DE genes using the mouse. A plot of differential expression per chromosome may highlight local regulatory biases hot spot loci. After download to 'txt' files, results can easily be converted and filtered in the Excel spreadsheet editor.
Couple of R basics Main R tutorial This one isn't too bad either. This methods and technology are very mature. First, save all of the CEL files you want to analyze in a single directory. Huge oversight by the Bioconductor guys - I may have just missed it if so, please let me know!!! Below is how I get it to work. The key is to find the annotation package for your array. For example, the mouse gene 2. You may have to do some google searching to find the correct package for your project.
Install it with biocLite "mogene20sttranscriptcluster. This assumes you already normalized the data, and the object "eset" has the data in it from above Load annotation library library mogene20sttranscriptcluster. To get specific fields, use packageName SYMBOL, where the caps part names the type of data you're after To get a list of available annotation information, run the packagename with at the end, i.