[BioC] Limma contrasts question

[James W. MacDonald]

Daniel Brewer wrote:
> Hello,
> 
> Apologies for not providing enough information.  It is a two colour
> Agilent array.  I was trying to keep the post short, but here is the
> full information (note I didn't design the experiment).
> 
> Targets file:
> SlideNumber	ArrayNumber	FileName	Name	Cy3	Cy5
> 1	1	Input/1_1.txt	1_1	Scramble	Knockdown
> 1	2	Input/1_2.txt	1_2	Knockdown	PC3M
> 1	3	Input/1_3.txt	1_3	PNT2	PC3M
> 1	4	Input/1_4.txt	1_4	Pooled	PNT2
> 2	2	Input/2_2.txt	2_2	PC3M	Scramble
> 2	3	Input/2_3.txt	2_3	PNT2	Scramble
> 3	1	Input/3_1.txt	3_1	PC3M	Pooled
> 3	2	Input/3_2.txt	3_2	Pooled	Knockdown
> 3	3	Input/3_3.txt	3_3	Scramble	Pooled
> 3	4	Input/3_4.txt	3_4	Knockdown	PNT2
> 
> PC3M = the control cell line
> Knockdown = PC3M with an siRNA knockdown vector
> Scramble = PC3M with a vector with a scrambled sequence
> PNT2 = Another cell line (not of interest here)
> Pooled = poll of knockdowns before you get specific clone, intermediate
> between PCM3 and knockdown - a hetrogenious group (not considered here)
> 
> Here is the relevant code (before this the standard steps were used to
> give normalised data without any controls):
> 
> #Differential analysis
> design <- modelMatrix(targets,ref="PC3M")
> fit <- lmFit(normNoC, design)
> contrast.matrix <- makeContrasts("Knockdown vs Controls"=(2*Knockdown -
> Scramble)/2, levels=design)
> fit2 <- contrasts.fit(fit, contrast.matrix)
> fit3 <- eBayes(fit2))
> 
>> design
>       Knockdown PNT2 Pooled Scramble
>  [1,]         1    0      0       -1
>  [2,]        -1    0      0        0
>  [3,]         0   -1      0        0
>  [4,]         0    1     -1        0
>  [5,]         0    0      0        1
>  [6,]         0   -1      0        1
>  [7,]         0    0      1        0
>  [8,]         1    0     -1        0
>  [9,]         0    0      1       -1
> [10,]        -1    1      0        0
> 
>> sessionInfo()
> R version 2.7.2 (2008-08-25)
> x86_64-pc-linux-gnu
> 
> locale:
> LC_CTYPE=en_GB.UTF-8;LC_NUMERIC=C;LC_TIME=en_GB.UTF-8;LC_COLLATE=en_GB.UTF-8;LC_MONETARY=C;LC_MESSAGES=en_GB.UTF-8;LC_PAPER=en_GB.UTF-8;LC_NAME=C;LC_ADDRESS=C;LC_TELEPHONE=C;LC_MEASUREMENT=en_GB.UTF-8;LC_IDENTIFICATION=C
> 
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
> 
> other attached packages:
> [1] limma_2.14.7
> 
> So I would like to compare the two controls (scramble and PNT2) with the
> knockdown, but I am not sure how to compare one variable in the model
> with two.

I assume you mean PC3M rather than PNT2. Now it depends on whether or 
not you want to assume (or maybe check) that the two controls are 
essentially the same, and hence want to pool, or if you want to do 
knockdown to each control separately.

For the former, this should suffice:

contrast.matrix <- makeContrasts("KvC" = Knockdown - (Scramble + 
PC3M)/2, levels = design)

So what we are asking for is the difference between the knockdown and 
the average of the two control samples.

An implicit assumption here is that the scramble and PC3M samples have 
similar expression values, so you might want to check by adding an 
additional term:

contrast.matrix <- makeContrasts("KvC" = Knockdown - (Scramble + 
PC3M)/2, "SvP" = Scramble - PC3M, levels = design)

If there are loads of significantly different genes in the SvP contrast, 
you might want to rethink the control status of these samples.

For the latter:

contrast.matrix <- makeContrasts("KvS" = Knockdown - Scramble
			"KvsC" = Knockdown - PC3M, levels = design)

Here we don't assume the scramble and PC3M samples are similar, and 
simply make comparisons individually. If you then wanted to see if the 
two control samples seemed similar, you could use decideTests() followed 
by vennCounts() and vennDiagram() to make a Venn diagram.

Best,

Jim


> 
> I would really appreciate your help with this.
> 
> Many thanks
> 
> Dan
> 

-- 
James W. MacDonald, M.S.
Biostatistician
Hildebrandt Lab
8220D MSRB III
1150 W. Medical Center Drive
Ann Arbor MI 48109-0646
734-936-8662