Using Bioconductor To Analyse Beadarray Data (lumi): Difference between revisions
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[[Category: R]] | [[Category: R]] | ||
[[Category: Bioinformatics]] | [[Category: Bioinformatics]] | ||
[[Category: Bioconductor]] | |||
==Software Requirements== | ==Software Requirements== | ||
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*At a minimum you need the Probe Profile data (normally a txt file). | *At a minimum you need the Probe Profile data (normally a txt file). | ||
*For all R procedures first change directory to your working directory then next create a new script, and save all executed lines in that script file. | *For all R procedures first change directory to your working directory then next create a new script, and save all executed lines in that script file. | ||
*Load the beadarray library, indictate dataFile (required | *Load the beadarray library, indictate dataFile (required) and mapping library (shown here is mouse) | ||
<pre> | <pre> | ||
data = "FinalReport_SampleProbe.txt" | data = "FinalReport_SampleProbe.txt" | ||
lumi = lumiR(data, lib='lumiMouseIDMapping') | |||
</pre> | </pre> | ||
*This fits the data into the lumi data frame. Phenotype data can be accessed by pData(lumi) and expression data can be accessed by exprs(lumi). | |||
*This fits the data into the | |||
==Data Normalisation== | ==Data Normalisation== | ||
*Microarray data is typically quantile normalised and log2 transformed: | *Microarray data is typically quantile normalised and log2 transformed: | ||
<pre> | <pre>lumi.N.Q = lumiExpresso(lumi)</pre> | ||
*To examine the effects of normalisation on the dataset use boxplots: | *To examine the effects of normalisation on the dataset use boxplots: | ||
<pre> | <pre> | ||