Hi Jim,

Thank you for the answer of p.value! and sorry for no codes in mail. The
followings are one example of my data and codes.

*Example *(partially copied from my result table when coef=1, 1st probeID):
t-statistic  P-value  Fold-change  mut.a         mut.a        wt1
wt2
108.84      0           10.28           9.90436     9.88196   10.2249
10.1021


*Codes*:
## data importing
library("affy")
library("limma")
targets <- readTargets("fed_total.txt")
aaa <- ReadAffy(filenames = targets $ filename)
eset <- rma(aaa)
pData(eset)
samples <- data.frame(genotype = c("wt", "wt", "mut.a", "mut.a","mut.b",
"mut.b"),replicate =c(1,2,1,2,1,2))
varInfo <- data.frame(labelDescription=c("wt, mut.a, mut.b", "arb
number"))
pd <- new("AnnotatedDataFrame", data = samples, varMetadata = varInfo)
phenoData(eset) <- new("AnnotatedDataFrame", data = samples, varMetadata =
varInfo)

## filtration
library("genefilter")
f1 <- anyNA
f2 <- pOverA(0.25, log2(100))
ff <- filterfun(f1, f2)
selected <- genefilter(eset, ff)
esetsub <- eset[selected,]

## limma fit and contrast
library("limma")
design <- model.matrix(~0+factor(c(1,1,2,2,3,3)))
colnames(design) <- c("wt", "mut.a","mut.b")
fit <- lmFit(ef, design)
fit2 <- eBayes(fit)
contrast.matrix <- makeContrasts("mut.a vs wt" = mut.a-wt,
                                 "mut.b vs wt" = mut.b-wt,
                                 "Diff" = (mut.a-wt)-(mut.b-wt),
                                 levels=design)
fit2 <- contrasts.fit (fit, contrast.matrix)
fit3 <- eBayes(fit2)

## find DEG and draw heatmap
x <- topTable(fit3, coef=1, adjust="fdr", sort.by="P", number=10000)
y <- x[x$adj.P.Val < 0.01,]
results <- decideTests (fit3, method="separate",
                        adjust.method="BH",p.value=0.01,lfc=1)

 ## Cluster and heatmaps ------ *draw in three ways, but confused with
results*
         ##1.   heatmap(as.matrix(esetsub[y$ID,]), scale="none")

           #2.   library("Heatplus")
                  heatmap_2(esetsub[y$ID,]),  scale="none")
           #3.  library(gplots)
                 heatmap.2(esetsub[y$ID,]), scale= "row", trace="none",
density.info="none")
               ## or
                 heatmap.2(esetsub[y$ID,]), scale= "none", trace="none",
density.info="none")

## html file output
library("affycoretools")
library("yeast2.db")
annotation(eset) <- "yeast2.db"
limma2annaffy(eset, fit3, design, adjust = "fdr",
        contrast.matrix, annotation(eset), pfilt = 0.01, fldfilt= 1)


*Questions*:
I have tons of questions about heatmap, and I sincerely appreciate if
anybody can give me some idea.
       1. what is the scale??  I know there are some discussion in the
maillist, but I am still confused. The result from scale=row is easy to
interpret since we can see some blocks across samples, but how it works??
       2.  when I change the coef=2, the heatmap result is totally
different, I know the coef=2 is comparing mut.b and wt, so what about the
same group of genes in mut.a?
       3. The result of limma contains logFC, AvrExprs, t-statistic, p
value,etc., which value that is used to draw cluster?? logFC or AveExprs??
Some papers their color key display fold change, does that mean I have to do
something to get the ratio before I draw heatmap?

4. same question with html table, how does the fold change generate?


Thank you very much!!!!!!!!!!
Hui-Yi


*sessionInfo()*
R version 2.7.2 (2008-08-25)
i386-pc-mingw32

locale:
LC_COLLATE=English_United States.1252;LC_CTYPE=English_United
States.1252;LC_MONETARY=English_United
States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252

attached base packages:
[1] splines   tools     stats     graphics  grDevices utils     datasets
[8] methods   base

other attached packages:
 [1] yeast2.db_2.2.0      affycoretools_1.12.1 annaffy_1.12.1
 [4] KEGG.db_2.2.0        biomaRt_1.14.1       RCurl_0.9-3
 [7] GOstats_2.6.0        Category_2.6.0       RBGL_1.16.0
[10] annotate_1.18.0      xtable_1.5-3         GO.db_2.2.0
[13] AnnotationDbi_1.2.2  RSQLite_0.7-0        DBI_0.2-4
[16] graph_1.18.1         affyPLM_1.16.0       gcrma_2.12.1
[19] matchprobes_1.12.1   genefilter_1.20.0    survival_2.34-1
[22] yeast2cdf_2.2.0      limma_2.14.6         affy_1.18.2
[25] preprocessCore_1.2.1 affyio_1.8.1         Biobase_2.0.1

loaded via a namespace (and not attached):
[1] cluster_1.11.11 XML_1.94-0.1



On Fri, Sep 26, 2008 at 8:32 AM, James W. MacDonald
<jmacdon@med.umich.edu>wrote:

> Hi Hui-Yi,
>
> Hui-Yi Chu wrote:
>
>> Hi everyone,
>>
>> Apologize first for probably simple question below.
>> Thanks for Jim's help, I've got some html files from limma2annaffy
>> function,
>> however, I am not pretty sure how to interpret the result of "fold
>> change".
>> Please correct me if I am wrong. Based on my knowledge, after limma, for
>> example in coef=1, the fold change represents the contrast of the
>> expression
>> value means from two groups. But the html result made me confused:
>>
>> The header of html is like this:
>> probes   Description   Pubmed   Gene Ontology   Pathway    t-statistic
>> P-value    Fold-change    array1   array1  array2   array2
>>
>>
>> The question is why the fold change is not really the value that (mean of
>> group1)- (mean of group2) ?? If it is because of permutation??
>>
>
> The fold change *is* mean(goup1) - mean(group2). If you show us your code
> and a snippet of the output we might be able to help (please see the posting
> guide).
>
>  I've read limma user guide and ?limma2annaffycore, so I believe the value
>> of
>> array 1 and 2 are expression value.
>> By the way, the function of pflt filter genes by p value, but if I want to
>> filter gene by adj.p.val??
>>
>
> I don't know of any function pflt. However, if you mean the argument pfilt,
> then it *does* filter by adjusted p-value if in fact you are adjusting the
> p-value to begin with.
>
> Best,
>
> Jim
>
>
>
>>
>> Thank you very much!!
>> Hui-Yi
>>
>>        [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>> Search the archives:
>> http://news.gmane.org/gmane.science.biology.informatics.conductor
>>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> Hildebrandt Lab
> 8220D MSRB III
> 1150 W. Medical Center Drive
> Ann Arbor MI 48109-0646
> 734-936-8662
>

	[[alternative HTML version deleted]]

