"The processed 'nor75 ' signal was obtained for every array by dividing the AFE-TGS by the 75th percentile of the signal for that particular array. This guarantees that the adjusted signals will all have a 75th percentile equal to 1. The reason for using the 75th percentile rather than other statistical measures such as the mean is to diminish the possible influence of outliers. The median could be used instead, but if we assume that about half of the genes will not show any significant expression, the 75th percentile will represent the median of the remaining 50% that are expressed."

https://bmcresnotes.biomedcentral.com/articles/10.1186/1756-0500-3-18

This might help:

https://github.com/Bioconductor-mirror/erccdashboard/blob/master/R/normalizeDat.R

Hello Somak,

Sorry for this extreme delayed reply. I was a bit busy with a manuscript so couldn't study microarray data analysis for a long time. Today I tried to normalize an agilent data using the R script you provided and got the following error:

"Error in if (sampleInfo$isNorm == FALSE) { : argument is of length zero"

Please help me to get rid of this. The step by step input functions which I used and their corresponding output is given below:

> targets library("limma", lib.loc="~/R/win-library/3.4")

> x source("../normalizeDat.r")

> normalizeDat(x)

Error in if (sampleInfo$isNorm == FALSE) { : argument is of length zero

>

Most likely your data format and the data format the R code accepts are not identical, particularly the column names

Hello Somak,

I tried to change the column names in my data file randomly to get the acceptable format, but it didn't work. Could you please provide a sample data file of acceptable format so that I can change my file accordingly.

This is a toy example:Modify accordingly

#Simulate a a toy set of 100 genes and 4 samples, assuming the abundance(raw intensity) follows a log-normal distribution

#in other words the log of the data follows a Gaussian (normal) distribution

set.seed(12345)

rawexp.df