# Constructs scatter plot to compare the effects of two normalization algorithms on a qPCR dataset.

### Description

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.

### Usage

1 | ```
plotVarMean(qpcrBatch1, qpcrBatch2, normTag1 = "Normalization Type1", normTag2 = "Normalization Type2", ...)
``` |

### Arguments

`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 |

### Details

For each gene, the function plots its log-transformed 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 log-transformed ratio of Gij to Gik on the y-axis, and the average expression of gene i on the x-axis 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.

### Value

A `plot`

object.

### Author(s)

Jess Mar jess@jimmy.harvard.edu

### References

Bolstad B et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 2003.

### See Also

`plot`

### Examples

1 2 3 4 | ```
# data(qpcrBatch.object)
# mynormRI.data <- normQpcrRankInvariant(qpcrBatch.object, 1)
# mynormQuant.data <- normQpcrQuantile(qpcrBatch.object)
# plotVarMean(mynormRI.data, mynormQuant.data, normTag1="Rank-Invariant", normTag2="Quantile", main="Comparing Two Data-driven Methods")
``` |