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Question: error when trying to apply the specific gene filter.
0
gravatar for alerodriguez
4 weeks ago by
alerodriguez0 wrote:

Please help me understand why I get an error when trying to apply the specific gene filter. Please see details below:

I am working with microarray data  dim()=54675    80 grouped by status

table(gcrma.ExpressionSet$TG.binary) =group1=52 and group2=28

My data is in log2 

#---------------------specific.filter------------------------

f2 <- ttest(gcrma.ExpressionSet$TG.binary, p=0.1)
wh2 <- genefilter(exprs(gcrma.ExpressionSet), filterfun(f2))
sum(wh2)

#---------------------specific.filter.error------------------------

Error in t.test.default(x = c(2.22237858766258, 2.22237858766258, 2.22237858766258,  : data are essentially constant

Thanks!

ADD COMMENTlink modified 19 days ago • written 4 weeks ago by alerodriguez0
1
gravatar for James W. MacDonald
4 weeks ago by
United States
James W. MacDonald44k wrote:

Using information about the experiment to filter your data is a horrible idea! The idea behind filtering is to reduce the multiple comparisons by excluding genes that are probably not being expressed, and are just contributing noise and not signal. You can do that by excluding genes with an average expression below some level, or by removing genes that have fewer than M out of N samples greater than some cutoff. But selecting genes based on whether or not they have a large t-statistic and then testing for differential expression using a t-statistic will artificially bias your results towards the alternative. You should be using a filtering method that is agnostic to the groups you are filtering on.
 

ADD COMMENTlink written 4 weeks ago by James W. MacDonald44k

Thank you for your comment,something like this?

f1 <- kOverA(0.50, 3.5)

ffun <- filterfun(f1)

flrGene <- genefilter(geneExpr, ffun)

geneExpr<- geneExpr[flrGene, ]​

 

ADD REPLYlink modified 19 days ago • written 19 days ago by alerodriguez0
0
gravatar for alerodriguez
19 days ago by
alerodriguez0 wrote:

f1 <- kOverA(0.50, 3.5)

ffun <- filterfun(f1)

flrGene <- genefilter(geneExpr, ffun)

geneExpr<- geneExpr[flrGene, ]​

something like this?

ADD COMMENTlink modified 19 days ago • written 19 days ago by alerodriguez0

You don't need to post the same thing twice, and you certainly don't need to use the 'Add your answer' box to post a comment. And yes, that is one way you can filter.
 

ADD REPLYlink written 18 days ago by James W. MacDonald44k

Thanks for your answer, very helpful. Blessings. 

 

ADD REPLYlink written 18 days ago by alerodriguez0
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