qnNormalize: Perform quantile normalization
Description
Perform quantile normalization between arrays
Usage
qnNormalize(eData, snr, method = 'quantile', snThresh = 3, ties = TRUE)
Arguments
eData
matrix of gene expression values
snr
Optional signal/noise ratio. Only used for trimAMean method
method
The normalization method desired. Default method is quantile
snThresh
Signal/noise threshold (default = 3) to indicate presence or
absence of a probe signal
ties
handle values with same rank
Value
data matrix with quantile normalized data values
Details
This function performs various normalization for the array data. The default is
quantile normalization method (adapted from Bioconductor limma package). Other
normalization methods include median, mean, trimMean (trimmed mean), trimAMean
(mean with absent gene removed).
For the median normalizaiton, the median signal of each array is scaled to the same
value (this value is calculated to equal to the median of all values in the data).
The signal values for each array are then adjusted by the scaling factor.
For the mean normalization, the approach is similar to the median normalization
procedure except that the mean signal of each array is scaled to the same value
(this value is median of all signals in the data).
For the trimMean normalization, the approach is similar to the mean normalization
except that the mean for each array is calculated after trimming the top and botton
5% of signals (a total of 10% of values).
For the trimAMean normalization, the signal values for absent probes are not
considered. If the s/n of a probe is less than snThresh (default = 3), the expression
of the probe is considered not present (absent). The remaining values are then
trimmed (top and botton 2.5%, a total of 5%), and the mean value for each array
after trimming is scaled to the same value (median of all values in the data).
References
bioconductor limma package for quantile normalization