Hi everyone,

I am using limma to analyze my array data. 

I have 6 (Nimblegen) arrays in total. They are two-color chip-on-chip arrays
without dye swaps.

3 arrays are male biological replicates and 3 female biological replicates.
I try to compare them and find sex-related differences (I have a pretty good
idea of what I am expecting to see and about 2% of the spots should be
different) and I also want to analyze them all together and see what is
enriched in both male-female after immunoprecipitations.

 

For the time being I chose to treat them as two-channel arrays (although I
am considering the option of doing single channel analysis).

 

I have two questions regarding the analysis (I apologize for my poor
knowledge of statistics!):

 

1)       Is there a way to use all six arrays for the normalization (I am
doing both within- and between-array normalization) and for the linear fit
model and then separate the male/female again to do eBayes? The reason I
would like to do this is that I observed (as expected) that by using a group
of 6 arrays gives much better statistics at the end than normalizing
separately for the 3 male and 3 female (the differences between them are not
great), but I want to keep the male/female data separate so that I use the
same processed dataset when I am doing the male/female comparison and when I
am searching for consistent ChIP enrichment across the sexes.

 

2)       My second question is regarding the calculation of the consensus
correlation to enter in the function  lmFit(). As I already mentioned I have
Nimblegen arrays, thus each gene is printed once but I do have triplicate
biological samples. So I thought that the command:

 

a<-duplicateCorrelation(MA, design=design, block=c(1,1,1,2,2,2)) 

should work, but what I got was:

 

> a$cor

[1] 1

> range(a$atanh.correlations)

[1] Inf Inf 

 

AND

(1)
MA<-normalizeWithinArrays(RG,RG$printer,method="control",controlspots=CONTRO
Lpos2)

(2)> show(RG)

An object of class "RGList"

$R

         A_M     B_M     F_M     C_F     D_F     E_F

[1,] 1603.22  697.67 1079.44  998.67  657.56 1849.00

[2,] 1874.22 1339.56 1331.89 1158.56 1146.00 1749.22

[3,] 1306.67 2033.00 1733.22  691.89 1814.56 1845.33

[4,]  827.00 1417.33 2237.22  694.56 1425.67 1650.56

[5,] 1052.22  565.44 1159.56  933.44  707.56 1091.11

389302 more rows ...

 

$G

         A_M     B_M     F_M     C_F     D_F     E_F

[1,]  521.89  632.33 1402.44  466.56 2093.33 1918.00

[2,]  863.11 1497.22 1733.33 1539.22 2331.56 2510.44

[3,]  730.78 5007.89 1898.56  706.78 4404.00 4502.78

[4,] 1437.44 2538.89 2860.67 1986.11 3349.33 2622.33

[5,]  720.33  779.44 1283.78  830.78 1788.00 1598.00

389302 more rows ...

 

$targets

  ChIP.ID FileName   Cy3 Cy5

1   95159  A_M.txt input ChIP

2  101711  B_M.txt input ChIP

3  101922  F_M.txt input ChIP

4   95854  C_F.txt input ChIP

5  104599  D_F.txt input ChIP

6  101923  E_F.txt input ChIP

 

$source

[1] "generic"

 

$printer

  ngrid.r ngrid.c nspot.r nspot.c

1  389307       1  389307       1

 

$genes

[1] "CHR1:3527111-3527575" "CHR1:3527111-3527575" "CHR1:3527111-3527575"
"CHR1:3527111-3527575" 

 

"CHR1:3527111-3527575"

 

(3)  > design

    MALE FEMALE

A_M    1      0

B_M    1      0

F_M    1      0

C_F    0      1

D_F    0      1

E_F    0      1

 

 


Can what I am getting (ie cor=1, range=inf) be correct? And if not any ideas
of what the problem might be and how I should deal with it?

 

> sessionInfo()

R version 2.4.1 (2006-12-18) 

i386-pc-mingw32 

 

locale:

LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United
Kingdom.1252;LC_MONETARY=English_United
Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252

 

attached base packages:

[1] "stats"     "graphics"  "grDevices" "utils"     "datasets"  "methods"  

[7] "base"     

 

other attached packages:

  limma 

"2.9.1"

 

 

 

Thanks

Niki Athanasiadou

 

ICMB

University of Edinburgh

 

 

 

 


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