**0**wrote:

Hi !

I need to compare the transcriptome of 5 cases and 5 controls using one-color Agilent microarrays. Weights were computed by the following function:

#My weight function

myFlagFun <- function(x) {

#Weight only strongly positive spots 1, everything else 0

present <- x$gIsPosAndSignif == 1

probe <- x$ControlType == 0

manual <- x$IsManualFlag == 0

strong <- x$gIsWellAboveBG == 1

y <- as.numeric(present & probe & manual & strong)

#Weight weak spots 0.5

weak <- strong == FALSE

weak <- (present & probe & manual & weak)

weak <- grep(TRUE,weak)

y[weak] <- 0.5

#Weight flagged spots 0.5

sat <- x$gIsSaturated == 0

xdr <- x$gIsLowPMTScaledUp == 0

featureOL1 <- x$gIsFeatNonUnifOL == 0

featureOL2 <- x$gIsFeatPopnOL == 0

flagged <- (sat & xdr & featureOL1 & featureOL2)

flagged <- grep(FALSE, flagged)

good <- grep(TRUE, y==1)

flagged <- intersect(flagged, good)

y[flagged] <- 0.5

y

}

Then I used the following script:

RG <- read.maimages(targets, source="agilent",green.only=TRUE, wt.fun=myFlagFun)

RGb <- backgroundCorrect(RG, method="none")

MA <- normalizeBetweenArrays(RGb, method="quantile")

MA.ave <- avereps(MA, ID=MA$genes$ProbeName)

f <- factor(targets$Condition, levels = unique(targets$Condition))

design <- model.matrix(~0 + f)

colnames(design) <- levels(f)

fit <- lmFit(MA.ave, design)

If I understood correctly, the weights are used in gene-wise weighted least squares regression to estimate the coefficients in the linear model. So they will affect the logFC values. My problem is that I need to get back to normalized individual measures so that their means are in line with coefficients calculated by the lmFit function.

Here is an example of what I get on a single gene:

In controls:

MA.ave values | 5.61618123133188 | 5.0746766862945 | 4.8972404255748 | 4.90207357931074 | 5,68229237143083 |

MA.ave weights | 1 | 0.5 | 0.5 | 0 | 1 |

The corresponding coefficient is

5.42814405
The corresponding coefficient is
Please let me know how I can get back to individual normalized measures taking into account the attributed weights ? Their means should be similar to coefficients reported in the fit object. Thank you very much for your help, Best Regards, Stephane |