This function makes a scatter plot which serves as a useful exploratory tool in evaluating whether one normalization algorithm has been more effective than another on a given qPCR dataset.
1  plotVarMean(qpcrBatch1, qpcrBatch2, normTag1 = "Normalization Type1", normTag2 = "Normalization Type2", ...)

qpcrBatch1 
A 
qpcrBatch2 
A 
normTag1 
Character string denoting what normalization algorithm was used for this data set. 
normTag2 
Character string denoting what normalization algorithm was used for this data set. 
... 
Further arguments can be supplied to the 
For each gene, the function plots its logtransformed ratio of its expression variance in one normalized dataset versus another normalized dataset, i.e. let Gij be the variance of the expression values of gene i that have been normalized with method j. We plot the natural logtransformed ratio of Gij to Gik on the yaxis, and the average expression of gene i on the xaxis for all genes. /cr The red curve represents a smoothed lowess curve that has been fitted to reflect the overall trend of the data. When the red curve drops below y = 0 (the blue dotted line) we know that method j effects a greater reduction in the variation of the data over method k. Similarly, when the red curve is above y = 0, method k is more effective in reducing the variation in the data than method j. If the data from both methods have similar variances then the red curve should remain at y = 0. Bolstad et al. (2003) originally used these plots for variance comparisons of different normalization methods for high density oligonucleotide array data.
A plot
object.
Jess Mar jess@jimmy.harvard.edu
Bolstad B et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 2003.
1 2 3 4  # data(qpcrBatch.object)
# mynormRI.data < normQpcrRankInvariant(qpcrBatch.object, 1)
# mynormQuant.data < normQpcrQuantile(qpcrBatch.object)
# plotVarMean(mynormRI.data, mynormQuant.data, normTag1="RankInvariant", normTag2="Quantile", main="Comparing Two Datadriven Methods")

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