#This script was written by Qi Zhao from Ren's Lab in SYSU.
#If you have any questions or suggestions, please contact the author by emali: zhaoqi3@mail2.sysu.edu.cn for details
#Do not support design without replication
#SAMSeq performs a Wilcoxon test for each transcript testing
#the counts of one condition against the counts of the other.
#Because standard normalization techniques are not applicable,
#subsampling is used to normalize the read counts.
#SAMSeq requires a relatively high number of samples
#per condition to obtain significance for differential expression.
#SAMseq(x, y, censoring.status = NULL,
#resp.type = c("Quantitative", "Two class unpaired", "Survival", "Multiclass", "Two class paired"),
#geneid = NULL, genenames = NULL, nperms = 100,
#random.seed = NULL, nresamp = 20, fdr.output = 0.20)
library(samr)
getsamfit<-function(data,conditionlist,myresp.type="Two class unpaired",mynperms = 100, mynresamp = 20, myfdr = 0.20){
y=as.numeric(factor(conditionlist,labels = c(1:length(unique(conditionlist)))))
samfit <- SAMseq(data.matrix(data), y, resp.type =myresp.type,fdr.output=myfdr,geneid=row.names(data),genenames=row.names(data),nresamp = mynresamp)
}
# getSamResultTable<-function(data,conditionlist,resp.type="Two class unpaired",myfdr=1){
# #data must be rounded
# #y must be a numberic vector
# samfit <- getsamfit(data,conditionlist,myresp.type=resp.type,fdr=myfdr)
# result<-data.frame(samfit$siggenes.table$genes.up)
# result2<-data.frame(samfit$siggenes.table$genes.lo)
#
# return(rbind(result,result2))
# }
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