[BioC] Is there an expected coefficient of variation for the hybridization probe intensities among technical replicates?

[Thornton, Matthew]

Hello!

I am digging through the control intensities for technical replicates obtained with an Affymetrix GeneChip Rat Gene 2.0 ST array.

I have 3 technical replicates of the same sample. The kernel density plot overlay of the raw data shows differences in the intensity distributions.

When I pull out the raw intensities with grepl("^AFFX", GeneName), I get the recorded intensities for each pixel mapped to a TranscriptClusterID corresponding to the particular AFFX control, some are 20 pixels others are 11.  The groups of AFFX controls I get are the hybridization controls: AFFX-BioB, AFFX-BioC, AFFX-BioDn, 
AFFX-CreX; then the PolyA controls:AFFX-DapX, AFFX-LysX, AFFX-PheX, AFFX-ThrX, AFFX-TrpnX (?! not listed anywhere, is it a PolyA or hyb?), AFFX-r2-Bs-dap, AFFX-r2-Bs-lys, AFFX-r2-Bs-phe, and AFFX-r2-Bs-thr. Another set of PolyA controls: AFFX-r2-Ec-bioB, AFFX-r2-Ec-bioC, AFFX-r2-Ec-bioD, AFFX-r2-P1-cre.  I also get the ERCC spike-in controls (which we did use). The maximum intensity from all controls is 25,069 grey levels the average is 1500 grey levels. What is a "good" average intensity?

If I calculate the coefficient of variation across each row in my data set, which corresponds to each X,Y intensity measurement across my replicates for each probe.  I see an average coefficient of variation at 22% just for the hybridization controls. Is this consistent with the experience of the list? What should be the expected coefficient of variation for the hybridization controls across technical replicates? the coefficient of variation is very similar for samples that show very low intensity values which are probably not expressed and for samples close to the average intensity. Even though I haven't done it, I don't think excluding probes based on a threshold with improve the average, because the numbers are uniformly similar.

Here is my code:
#!/usr/bin/Rscript --vanilla --slave

# Load Libraries
library(Biobase)
library(xps)

# Directory to store ROOT scheme files
scmdir <- "/data/met/scmdir/"
scheme_RaGene <- root.scheme(file.path(scmdir, "scheme_RaGene20stv1.root"))

# Collect scheme information - Pulls out information from the Root scheme
ann <- export(scheme_RaGene, treetype="ann", outfile="scheme_RaGene_ann.txt", as.dataframe=TRUE, verbose=FALSE)
scm <- export(scheme_RaGene, treetype="scm", outfile="scheme_RaGene_scm.txt", as.dataframe=TRUE, verbose=FALSE)

# more file locations
datdir <- "/data/met/26Feb14_TR_CTRs/"
celdir <- "/data/met/26Feb14_TR_CTRs/CEL/"
celfiles <- c("CTR_TR1.CEL", "CTR_TR2.CEL", "CTR_TR3.CEL")
celnames <- c("CTR_TR1", "CTR_TR2", "CTR_TR3")
outdir <- "/data/met/26Feb14_TR_CTRs/"

# Import CEL files and begin
data_raw <- import.data(scheme_RaGene, "raw_data_CTR", filedir=outdir, celdir=celdir, celfiles=celfiles, celnames=celnames, verbose=TRUE)

# Attach info
data_raw <- attachMask(data_raw)
data_raw <- attachInten(data_raw)

tmp <- intensity(data_raw)

write.csv(tmp, file="raw_data.csv", row.names=FALSE)

# Save an image
save.image(file="24Feb14_1.RData")

##
## Manipulation of "Raw Data"
##

# plots
png(file="Raw_Data_Density_Plot.png", width=600, height=600)
par(mar=c(6,3,1,1));
hist(data_raw, add.legend=TRUE)
dev.off()

# Get AFFX controls for assessment of PolyA, ERCC, and Hyb
annot_raw <- merge(tmp, scm, by=c("X", "Y"), all.x=TRUE)
annot_raw2 <- merge(annot_raw, ann, all.x=TRUE)
raw_data <- annot_raw2[ order(annot_raw2[,3], annot_raw2[,2]), ]
write.csv(raw_data, file="raw_data_annotated.csv", row.names=FALSE)

# get the only handle for ERCC
set.affx <- subset(raw_data, grepl("^AFFX", GeneName))
write.csv(set.affx, file="affx_controls_raw.csv", row.names=FALSE)

# I usually open the csv file with a spreadsheet program delete the columns that I don't need and calculate the coefficient of variation for each set.

Any comments, advice, or information are greatly appreciated!! 

Matt