[BioC] Single Channel Analysis in Limma using lmscFit

[Brett Abrahams]

Hello,

I would like to use limma to carry out single channel analyses on some two 
color data (to address comparisons that can't be made with standard methods 
and my unconnected experimental design). I'm able to get two color analyses 
to work nicely but run into problems when I try to run the single channel 
analysis using the 'lmscFit' function (as described in the March 9 2005 
user's guide) .

Everything seems to work fine until I run 'intraspotCorrelation' which 
results in a number of errors (see Point 1 below for input/output). I've 
looked into the versions of limma (Version: 1.8.22) and statmod (Version: 
1.1.0) but this doesn't seem to be the answer as both are current. Any 
thoughts on why these error messages are being generated and what I can do 
to fix the problem would be much appreciated. Also, am I right in thinking 
the 'reml' errors I get refer to problems with only single genes? I picked 
this up from a previous post to the list but may have misinterpreted.

If I ignore the errors and carry on with the analysis through to the 
topTable I get more results that I can't understand (see Point 2 below 
input/output). What's confusing me here is that although results from the 
'decideTests' function seems to suggest that differentially expressed genes 
are present within each of the four contrasts I've specified, only one of 
the four corresponding topTables shows anything with significant p values. 
Amongst the contrasts without significant differences most p values 
are >0.5 and all B values are negative. Any clarification would be great.

Thanks in advance for this wonderful software and superb documentation / 
support.

Bret

Point 1
 > corfit <- intraspotCorrelation(MA, design)

Loading required package: statmod

Attaching package 'statmod':

The following object(s) are masked from package:limma :

matvec vecmat

Warning messages:

1: reml: Max iterations exceeded in: remlscore(y, X, Z)
2: reml: Max iterations exceeded in: remlscore(y, X, Z)
3: reml: Max iterations exceeded in: remlscore(y, X, Z)

 >

Point 2

Two groups (G1 and G2) with two tissues examined for each

 > results <- decideTests(fit, method="nestedF")

 > summary(results)
g1-g2	t1-t2	g1t2 - g2t2	g1t1-g2t1
-1    29   936      30          14
0  17866 16356   17861       17891
1     60   663      64          50

 > topTable(fit3, coef=1, adjust="fdr")
 > topTable(fit3, coef=1, adjust="fdr")

  Status          M        A         t   P.Value         B
15540   gene  0.4363826 8.514271  5.138324 0.6918086 -1.734635
10050   gene -0.3957220 9.904263 -4.802365 0.6918086 -1.936940
13504   gene  0.4280439 8.957909  4.492316 0.6918086 -2.136429
9953    gene  0.3380621 8.555516  4.486686 0.6918086 -2.140164
3266    gene  0.4737100 6.280065  4.478957 0.6918086 -2.145299
5596    gene  0.3738408 8.360172  4.327564 0.6918086 -2.247386
18159   gene -0.3860020 6.880403 -4.272878 0.6918086 -2.284962
6550    gene  0.4545545 7.901485  4.258022 0.6918086 -2.295233
8589    gene -0.4707301 7.850181 -4.227162 0.6918086 -2.316657
10149   gene  0.3387359 9.005838  4.216223 0.6918086 -2.324278