[BioC] Bioconductor

[Laurent Gautier]

An innocent remark about the sequence of indexing you are "not sure about but
that works for you". There have been a "very similar" piece of code circulating
around. It works for others.
 (if time to kill, try to google on 'summary aov "[1]][4:5][[2]][1]"').


Hopin' it helps,


Laurent




On Wed, May 21, 2003 at 03:18:43PM +0100, Claire Wilson wrote:
> Hi Richard,
> 
> For t-tests, this is what I do:
> 
> do.ttest <- function(y, x1=1, x2=2) {
> 	# split y according to categories in pData
> 	# For example you may have cell.line as a cateogry in pData
> 	# because the function is called with esApply, it knows the values of pData
> 	# therefore knows that cell.line is one of the categories
> 	# defaults for x1 and x2 are set as the first two categories
> 	# y[[1]]= cell line a, y[[2]] = cell line b, y[[3]] = cell line c, y[[4]] = cell line d
>             exprs.values <- split(y, cell.line)
> 	# Do a t-test between the expression values of cell line a and cell line b
> 	# Return the p-values
> 	t.test(exprs.values[[x1]], exprs.values[[x2]])$p.value
>   }	
> 
> # Function is called as follows
> # Changing x1 and x2 means that you can change which two cell lines are analysed
> # 1 means analyse eset row-wise
> > t.res <- esApply(eset,1, do.ttest, x1=1, x2=2)
> # t.res holds a list of probe set ids and their p-values
> 
> For anova:
> calc.anova.pval <- function(x) {
>   	# [[1]] takes us to the summary information
>   	# [4:5] are columns in the summary data that contain the F-value and the P-value
>  	# [2] selects the P-value and [1] says the 1st row of P-value 
> 	# Not too sure on the details of the numbers but it works for me!
> 	# Again I call it with esApply, so it knows what the pData categories are
>  	summary(aov(x ~cell.line))[[1]][4:5][[2]][1]
>  }
> 
> # Call the function and store the anova p-values as a list with the probeset ids
> > anova.pvalues <- esApply(eset, 1, calc.anova.pval)
> 
> # Another way to do ANOVA, but you don't get the p-values back
> # Returns a logical vector which indicates whether the p-value from the ANOVA
> # is less than a specified value
> # Declare the filter
> # Test the expression levels in eset according to cell.line
> # Return true for all those with a p-value <= 0.01
> > anova.filter <- Anova(eset$cell.line, p=0.01)
> 
> # Apply the filter, but you don't get the raw p-values, therefore can't filter according to p-value
> > signif.anova.01 <- genefilter(exprs(eset), filterfun(anova.filter))
> 
> None of the above have included multiple testing, which is necessary if you are analysing large numbers of probesets - See the Bioconductor multtest package.
> 
> Hope this helps/makes sense/is right!
> 
> claire
>  --
> Claire Wilson   
> Bioinformatics group  
> Paterson Institute for Cancer Research  
> Christies Hospital NHS Trust  
> Wilmslow Road,  
> Withington  
> Manchester  
> M20 4BX  
> tel: +44 (0)161 446 8218  
> url: http://bioinf.picr.man.ac.uk/
>  
> --------------------------------------------------------
> 
>  
> This email is confidential and intended solely for the use of th... {{dropped}}
> 
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-- 
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currently at the National Yang-Ming University in Taipei, Taiwan
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Laurent Gautier			CBS, Building 208, DTU
PhD. Student			DK-2800 Lyngby,Denmark	
tel: +45 45 25 24 89		http://www.cbs.dtu.dk/laurent