One of the best features of Bioconductor is the built in annotation of microarray probes. This makes it veryeasy to query a large number of databases to test out any potential biological explanations for a particular expression profile.
The following code snippet will take the top values above a p-value threshold for the corrected multiple testing (if Benjamini-Hochberg is used this will be the false discrovery rate) and produce an HTML file that summarises the significant results.
library("KEGG.db")
library("GO.db")
library("annaffy")
library("XML")
library("annotate")
library("hgu133a.db")
afarms1<-topTable(fit2farms, coef=1, adjust="BH", n=100, p.value=0.01)
gnfarms <-as.character(afarms1$ID)
symfa1 <-getSYMBOL(gnfarms, "hgu133a")
symfa1
atest <- aafTableAnn(gnfarms, "hgu133a.db", aaf.handler())
saveHTML(atest, file="reportfarms1.html")
The following code snippet will take the top values above a p-value threshold for the corrected multiple testing (if Benjamini-Hochberg is used this will be the false discrovery rate) and produce an HTML file that summarises the significant results.
library("KEGG.db")
library("GO.db")
library("annaffy")
library("XML")
library("annotate")
library("hgu133a.db")
afarms1<-topTable(fit2farms, coef=1, adjust="BH", n=100, p.value=0.01)
gnfarms <-as.character(afarms1$ID)
symfa1 <-getSYMBOL(gnfarms, "hgu133a")
symfa1
atest <- aafTableAnn(gnfarms, "hgu133a.db", aaf.handler())
saveHTML(atest, file="reportfarms1.html")
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