Nothing

```
ExClust_compute_stats <- function(gene.IDs=NULL, gene.PVals=NULL){
### first we need to grab the gene names & p-values for each gene
# grab the 1st row of each gene
pvalue.Indices <- match(unique(gene.IDs), gene.IDs)
# now grab the gene IDs from these rows
NamesGenePVals <- gene.IDs[pvalue.Indices]
# lastly grab the p-values from these rows
GenePVals <- gene.PVals[pvalue.Indices]
### ExCluster progression message
message("Running statistical tests ...","",sep="\n")
### data frame for testing p-values
Catch_PVals <- data.frame(NamesGenePVals,GenePVals)
colnames(Catch_PVals) <- c("EnsG","pvals")
# remove NAs
Catch_PVals <- Catch_PVals[which(complete.cases(Catch_PVals[,2])),]
# sort PVals from lowest to highest
Catch_PVals <- Catch_PVals[order(Catch_PVals[,2]),]
if (nrow(Catch_PVals) >= 5000){
### normalize p-values
# estimate the number of 'truly spliced genes'
pvalCutoff <- EstNullHypo(Catch_PVals[,2])+0.005
# estimate the number of truly spliced genes (may include some false positives)
NumTrue <- length(which(Catch_PVals[,2] <= pvalCutoff))
# estimate the number of null hypothesis genes
NumNull <- length(which(Catch_PVals[,2] > pvalCutoff))
### if we have > 0 NumTrue p-values
if (NumTrue > 0){
# Now give the null hypothesis p-values uniform distriubtions
Catch_PVals[seq((NumTrue+1),(NumTrue+NumNull)),2] <- sort(runif(n=NumNull,0,1))
# Now divide all true p-values by a factor based on the lowest null hypothesis value
Catch_PVals[seq(NumTrue),2] <- Catch_PVals[seq(NumTrue),2]/
(Catch_PVals[NumTrue,2]/Catch_PVals[(NumTrue+1),2])
# Now compute FDR values
FDRs <- FDRcalc(Catch_PVals[,2],NumTrue,NumNull)
}else{
### if we have no truly significant p-values from our estimate
Catch_PVals[,2] <- sort(runif(n=NumNull,0,1))
FDRs <- rep(1,nrow(Catch_PVals))
}
}else{
### This part of the code will be run if less than 5000 expressed genes were identified
warning(call = ExCluster_errors$too_few_pvalues)
# because we have too few pvalues (< 5000), we cannot run our advanced stats
# we therefore run a basic correction with Benjamini-Hochberg
pvalCutoff <- 0.025
# estimate the number of truly spliced genes
NumTrue <- nrow(Catch_PVals[Catch_PVals[,2] <= pvalCutoff,])
# estimate the number of null hypothesis genes
NumNull <- nrow(Catch_PVals[Catch_PVals[,2] > pvalCutoff,])
# set the minimum value a null hypothesis p-value can be corrected to
minNullPVal <- 0.005
### if we have truly differentially spliced genes
if (NumTrue > 0){
# Now give the null hypothesis p-values uniform distriubtions
Catch_PVals[seq((NumTrue+1),(NumTrue+NumNull)),2] <- sort(runif(n=NumNull,minNullPVal,1))
# Now divide all true p-values by a factor based on the lowest null hypothesis value
Catch_PVals[seq(NumTrue),2] <- Catch_PVals[seq(NumTrue),2]/(Catch_PVals[NumTrue,2]/minNullPVal)
# Now compute FDR values
FDRs <- p.adjust(p = Catch_PVals[,2],method="BH")
}else{
### if we have no truly significant p-values from our estimate
Catch_PVals[,2] <- sort(runif(n=NumNull,0,1))
FDRs <- rep(1,nrow(Catch_PVals))
}
}
### Make statistics table per gene with pvals and FDRs
Stats_table <- data.frame(Catch_PVals,FDRs)
return(Stats_table)
}
```

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