Hi all,

I am using limma package to analyze the expression array data (single channel Agilent microarray) of 14 paired dog cancer samples. 

library(limma)
SDRF <- read.delim("SDRF.txt", check.names=FALSE, stringsAsFactors=FALSE)
x <- read.maimages(SDRF[,"Array Data File"],source="agilent",green.only=TRUE)
y <- backgroundCorrect(x,method="normexp")
neg95 <- apply(y$E[y$genes$ControlType==-1,],2,function(x) quantile(x,p=0.95))
cutoff <- matrix(1.1*neg95,nrow(y),ncol(y),byrow=TRUE)
isexpr <- rowSums(y$E > cutoff) >= 7
table(isexpr)
y0 <- y[y$genes$ControlType==0 & isexpr,]
group <- SDRF[,"Dog"]
levels <- c("1","2","3","4","5","6","7")
group <- factor(group, levels = levels)
type <- SDRF[,"Type"]
levels <- c("P","M")
type <- factor(type, levels = levels)
design <- model.matrix(~group+type)

It works fine. However, when I ran

fit <- lmFit(y0, design)

It shows that

Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x),  : 
  'data' must be of a vector type, was 'NULL'

This problem is similar with the one posted at Agilent data processing by Limma R package, but has not been solved yet. 

> traceback()
5: array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), 
       NULL) else NULL)
4: as.matrix.default(object$M)
3: as.matrix(object$M)
2: getEAWP(object)
1: lmFit(y0, design)

I am a newbie and still have no idea what's wrong with the function. It is highly appreciated if someone can help.

Here is the system I am using:

> sessionInfo()
R version 3.2.4 (2016-03-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.3 (El Capitan)

locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
[1] tools_3.2.4

Thanks a lot!

Jesse

There isn't anything wrong with the lmFit() function. The problem is that you haven't normalized the data, so that the data object y0 is not yet ready for lmFit().

Please follow the case study code in the limma User's Guide, which uses normalizeBetweenArrays().