[BioC] M value in topTable and Fold Change
Marcelo Luiz de Laia
mlaia at fcav.unesp.br
Wed Jun 15 22:05:32 CEST 2005
Dear limma users and all bioconductor's users,
I have four questions about the subject.
I analyzed our data in the following way:
todos <- read.matrix("todos.txt",sep="\t")
todos <- log2(todos)
todos.norm <- normalize.quantiles(todos)
design <- model.matrix(~-1+factor ......
cor <- duplicateCorrelation(......
todos.fit <- lmFit(todos.norm,design,.......
namegene <- read.table("nameGENE.txt"......
uniquegenename <- uniquegenelist(namegene,......
contrast.matrix <- makeContrasts(T-C,....
todos.fit2 <- contrasts.fit(todos.fit,.....
todos.fit3 <- eBayes(todos.fit2)
todos.fit3$genes <- uniquegenename
TC <- topTable(todos.fit3,number=100,coef=1,adjust="fdr")
First question: this script is OK?
Second question: Although already to have propagated here in the list
some messages in respect of this subject, none of them gave me
certainty. Therefore, I go to ask again, because I still have doubts.
Fold Change is the ratio between Treated (T) and Control (C). Correct?
Third question: In the majority of the cases, the data are transformed
into log in base 2. Then, in these cases, I have: (Log2 T / Log2 C).
Correct? In this case, Fold Change would be equal to 2^(Log2 T / Log2
Fourth question: Exists some case where the ratio between the T and C
are made before the Log transformation? For example: Log2(T/C).
In the way that I made our analyses, value "M", in topTable, would be
the described one in the third question? How it was gotten? First was
gotten average among repetitions of the treatment and average among
repetitions of the control and, then, obtained the ratio? I ask because
I made some empirical attempts and I did not obtain how the "M" value
was obtained. If this is very complicated, please, you are free to skip
it because I would not understand if it is very complicated.
My excuses for the long e-mail.
Thanks very much
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