[BioC] marray layout

[Vanessa Vermeirssen]

Dear,

I have some questions about the marray Layout object. I try to upload 
public microarray
data in R in order to create a microarray compendium.

I have used an SMD database file to create the targets, probes and data 
objects for marray.

The array has 21632 spots, however in the database file I only have 
information for 21504 spots.
Moreover, some of the probes are not matchable to a gene and therefore 
for those I would like to
neglect their expression values. I did this now by parsing the database 
file and setting the expression values
at NA for those "non-matchable" probes.

Hence I have 21504 probes, of which 801 are controls and 784 non-matchable
(NAs in their expression values).
I tried to declare this with maSub that I use only part of the array 
(21504 spots and not 21632).
My maControls only considers the 21504 spots.

I read in the whole thing and then look at the graphs created by one 
color "image". There seems to be a lot
of white spots in my arrays and they are not consistent in the same 
place on the array ( although all the NAs
refer to the same 784 probes in the 3 arrays, see code below).
I was wondering if this has something to do with this maSub... and that
it put these NAs (21632-21504, only about 100 ones) randomly on the array?
*How can I correctly read in my marrayLayout?*
I only want to use image to see if there is some spatial gradient in my 
arrays. If htere is, I need to use
background substraction, otherwise I won't.

This is the code I use...I am reading in 3 arrays of the format Chalfie379.
In blue the output I get from R.
The pdf file with the M images  can be found at 
http://www.psb.ugent.be/~vamei/Docs/Chalfie379_image.pdf

library(limma)
library(marray)
library("Biobase")

Chalfie379targets=read.marrayInfo("file_A-SMDB-379", info.id=c(2,4,5), 
labels=c(2))
summary(Chalfie379targets)
Object of class marrayInfo.

   maLabels      SMDfile         green Cy3 CH1               red Cy5 CH2
1 new17040.xls new17040.xls wt sorted touch cell  mutant sorted touch cell
2 new17041.xls new17041.xls wt sorted touch cell  mutant sorted touch cell
3 new17042.xls new17042.xls wt sorted touch cell  mutant sorted touch cell

Number of labels:  3 Dimensions of maInfo matrix:  3  rows by  3  columns

Notes:
file_A-SMDB-379

file379 <- as.vector(Chalfie379targets at maInfo[,1])
Chalfie379.gnames <- read.marrayInfo(file379[1],info.id=c(1,2,3), labels=2)
summary(Chalfie379.gnames)
Object of class marrayInfo.

maLabels Spot    Name      ID
1     AC3.5    1   AC3.5   AC3.5
2     AC3.8    2   AC3.8   AC3.8
3     AH6.6    3   AH6.6   AH6.6
4   B0001.3    4 B0001.3 B0001.3
5   B0024.1    5 B0024.1 B0024.1
6   B0024.4    6 B0024.4 B0024.4
7   C06H5.1    7 C06H5.1 C06H5.1
8   C07A9.5    8 C07A9.5 C07A9.5
9   C07G2.1    9 C07G2.1 C07G2.1
10  C08B6.4   10 C08B6.4 C08B6.4
...

Number of labels:  21504 Dimensions of maInfo matrix:  21504  rows by  
3  columns

Notes:
new17040.xls

Chalfie379.L <- new("marrayLayout", maNgr=8, maNgc=4, maNsc=26, maNsr=26)
maControls(Chalfie379.L) <- Chalfie379.gnames at maInfo[,3]
length(Chalfie379.L at maControls)
S <- c(1:maNspots(Chalfie379.L))
T <- Chalfie379.gnames at maInfo[,1]
Z <- S %in% T
maSub(Chalfie379.L) <- Z
ctl <- rep("control", length(T))
ctl[maControls(Chalfie379.L) != "control"] <- "probe"
maControls(Chalfie379.L) <- factor(ctl)
summary(Chalfie379.L)
Array layout:    Object of class marrayLayout.

Total number of spots:                  21632
Dimensions of grid matrix:              8 rows by 4 cols
Dimensions of spot matrices:            26 rows by 26 cols

Currently working with a subset of 21504spots.

Control spots:
There are   2 types of controls :

control   probe
 801   20703


Notes on layout:

Chalfie379.raw <- read.marrayRaw(file379,name.Gf="CH1_I", 
name.Gb="CH1_B", name.Rf="CH2_I",
name.Rb="CH2_B", gnames=Chalfie379.gnames, layout=Chalfie379.L, 
targets=Chalfie379targets, DEBUG=TRUE)

pdf("Chalfie379_image.pdf")
image(Chalfie379.raw, col=1, main="1")
image(Chalfie379.raw[,2], col=1, main="2")
image(Chalfie379.raw[,3], col=1, main="3")
dev.off()

myM <- Chalfie379.raw[,2]@maGf /Chalfie379.raw[,2]@maRf
sum(is.na(myM))
[1] 784

 > summary(which(is.na( Chalfie379.raw[,3]@maGf )))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  22    5564   10690   10770   15920   21450
 > summary(which(is.na( Chalfie379.raw[,2]@maGf )))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  22    5564   10690   10770   15920   21450
 > summary(which(is.na( Chalfie379.raw[,1]@maGf )))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
  22    5564   10690   10770   15920   21450

Thank you so much for your help!

Best regards,
Vanessa Vermeirssen

-- 

-- 
==================================================================
Vanessa Vermeirssen, PhD

Tel:+32 (0)9 331 38 23                        fax:+32 (0)9 3313809
VIB Department of Plant Systems Biology, Ghent University
Technologiepark 927, 9052 Gent, BELGIUM
vamei at psb.ugent.be                         http://www.psb.ugent.be