[BioC] Single Channel Analysis

[Naomi Altman]

Yes, I was aware that the contrasts compare all other treatments to 
"C".  Thanks for your prompt reply.

--Naomi

At 05:29 PM 11/15/2005, Gordon Smyth wrote:
>Dear Naomi,
>
>At 06:20 AM 16/11/2005, Naomi Altman wrote:
>>Hi Gordon,
>>I just used the single channel analysis for a loop design.  I am 
>>wondering if it is possible that there is a code error that swaps 
>>the roles of Cy3 and Cy5.  The results did not
>>make sense when I fitted with as shown below.
>>
>>Cy5=c("S ","SB ","F ","St ","MB ","C ","P ","L ","C ",
>>"MB ","St ","F ","SB ","S ","L ","P ")
>>Cy3=c("MB ","C ","P ","L ","St ","S ","SB ","F ",
>>"St","F ","P ","C ","MB ","L","SB","S")
>>targets=data.frame(RG$targets,Cy3=Cy3,Cy5=Cy5)
>>TargetArray=targetsA2C(targets)
>>
>>MAb <- normalizeBetweenArrays(MA,method="Aq")
>>design <- model.matrix(~Target,data=TargetArray)
>>corfit <- intraspotCorrelation(MAb,design)
>>fit <-
>>lmscFit(MAb,design,correlation=corfit$consensus)
>>cont.matrix <- rbind(rep(0,7),diag(1,7))
>>fit2 <- contrasts.fit(fit,cont.matrix)
>
>Your contrasts compare all the other treatments vs "C", because "C" 
>is alphabetically sorted first and becomes the first level of 
>TargetArray$Target. Is that what you intended?
>>But when I used
>>targets=data.frame(RG$targets,Cy3=Cy5,Cy5=Cy3)
>>
>>the results make sense (we know what several of the genes do and 
>>they show up in the right place this way) and the estimated error 
>>variance is cut in half. I can tediously go through and compute the 
>>some of the tissue means by hand to compare with the lmscFit 
>>output, but I thought you would know where in the code to check this.
>>
>>Thanks,
>>
>>Naomi S. Altman                                814-865-3791 (voice)
>>Associate Professor
>>Dept. of Statistics                              814-863-7114 (fax)
>>Penn State University                         814-865-1348 (Statistics)
>>University Park, PA 16802-2111
>
>
>The lmscFit() code has been well tested, and I like to think that 
>such a drastic error as swapping Cy3 and Cy5 would have been 
>eliminated right at the beginning. Here is a little analysis, taken 
>from example(lmscFit) to show that lmscFit gives the same results as 
>an ordinary log-ratio analysis, just with more significance.
>
>Another similar example is at:
>https://stat.ethz.ch/pipermail/bioconductor/2004-August/005607.html
>
>Regards
>Gordon
>
> > library(limma)
> > library(sma)
> > data(MouseArray)
> > RG <- backgroundCorrect(mouse.data, method = "half")
> > MA <- normalizeWithinArrays(RG, mouse.setup)
> > MA <- normalizeBetweenArrays(MA, method = "Aq")
> > targets <- data.frame(Cy3 = I(rep("Pool", 6)), Cy5 = I(c("WT", 
> "WT", "WT", "KO", "KO", "KO")))
> >
> > # Log-ratio analysis
> > design <- cbind(WTvsPool=1,KOvsWT=c(0,0,0,1,1,1))
> > fit <- eBayes(lmFit(MA,design))
> >
> > # Separate channel analysis
> > targets.sc <- targetsA2C(targets)
> > targets.sc$Target <- factor(targets.sc$Target, levels = c("Pool", 
> "WT", "KO"))
> > designsc <- model.matrix(~Target, data = targets.sc)
> > fitsc <- lmscFit(MA, designsc, correlation = 0.9)
> > cont.matrix <- cbind(KOvsWT = c(0, -1, 1))
> > fitsc2 <- contrasts.fit(fitsc, cont.matrix)
> > fitsc2 <- eBayes(fitsc2)
> >
> > topTable(fit,coef="KOvsWT")
>               M        A          t     P.Value          B
>2149 -2.9843122 12.29973 -17.730955 0.001779788 -0.1898774
>5356 -1.8056831 12.80482  -9.588158 0.067261480 -0.6726402
>2537 -1.0896738 13.59237  -8.988952 0.069578132 -0.7550846
>1739 -1.1223897 13.79072  -6.746311 0.347698716 -1.2148770
>1496 -0.9193937 12.24417  -6.330681 0.416827883 -1.3376946
>1337 -1.1629891 11.63667  -5.995650 0.488202522 -1.4484874
>540  -2.3767753 11.66888  -5.523154 0.692239877 -1.6252308
>4139 -1.0183999 12.45938  -5.389478 0.694271543 -1.6800493
>6319  0.8041058 10.95522   5.294233 0.694271543 -1.7205015
>4941 -1.0259822 13.36684  -4.956517 0.837701802 -1.8738335
> > topTable(fitsc2)
>               M         A          t      P.Value         B
>2149 -2.9612419 12.299725 -17.654541 1.388509e-06 7.9167816
>5356 -1.7778575 12.804818 -10.857724 2.453726e-04 5.7003388
>2537 -1.0408212 13.592375  -7.813258 5.146981e-03 3.6076740
>1739 -1.1411915 13.790725  -7.788565 5.146981e-03 3.5859380
>1496 -0.8907386 12.244169  -6.858207 1.569256e-02 2.6981643
>540  -2.3692707 11.668883  -6.204245 3.584067e-02 1.9879911
>4139 -1.0215328 12.459384  -5.764137 6.258656e-02 1.4671351
>4941 -1.0483388 13.366842  -5.022158 1.834206e-01 0.5085680
>6319  0.9151930 10.955218   4.933243 1.834206e-01 0.3869647
>5375  0.6655686  9.755678   4.924741 1.834206e-01 0.3752643
>
>

Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
University Park, PA 16802-2111