completeFeatures <- function(object1, qcThreshold1,
object2=NULL, qcThreshold2=NULL,
label1=NULL, label2=NULL){
object1 <- checkObject(object1)
object1$ct[object1$qc<qcThreshold1] <- NA
tst1 <- rowSums(is.na(object1$ct))
## xs <- as.numeric(gsub("KW","",gsub(":.+","",colnames(object1$ct))))
## sna <- t(apply(is.na(object1$ct),1,by,xs,sum))
if(is.null(object2)){
tab <- matrix(nrow=3, ncol=1)
tab[1,1] <- sum(tst1==0)
tab[2,1] <- sum(tst1>0 & tst1<ncol(object1$ct))
tab[3,1] <- sum(tst1==ncol(object1$ct))
rownames(tab) <- c("Complete miRNAs (all good quality & non-NA)",
"Partial miRNAs (some good quality & non-NA)",
"Absent miRNAs (no good quality & non-NA)")
colnames(tab) <- label1
} else{
object2 <- checkObject(object2)
object2$ct[object2$qc<qcThreshold2] <- NA
tst2 <- rowSums(is.na(object2$ct))
tab <- matrix(nrow=3, ncol=3)
tab[1,1] <- sum(tst1==0 & tst2==0)
tab[2,1] <- sum((tst1>0 & tst1<ncol(object1$ct)) & tst2==0)
tab[3,1] <- sum(tst1==ncol(object1$ct) & tst2==0)
tab[1,2] <- sum(tst1==0 & (tst2>0 & tst2<ncol(object2$ct)))
tab[2,2] <- sum((tst1>0 & tst1<ncol(object1$ct)) &
(tst2>0 & tst2<ncol(object2$ct)))
tab[3,2] <- sum(tst1==ncol(object1$ct) &
(tst2>0 & tst2<ncol(object2$ct)))
tab[1,3] <- sum(tst1==0 & tst2==ncol(object2$ct))
tab[2,3] <- sum((tst1>0 & tst1<ncol(object1$ct)) &
tst2==ncol(object2$ct))
tab[3,3] <- sum(tst1==ncol(object1$ct) & tst2==ncol(object2$ct))
if(is.null(label1)) label1 <- "Method 1"
if(is.null(label2)) label2 <- "Method 2"
rownames(tab) <- paste(label1, c("Complete", "Partial", "Absent"),
sep=":")
colnames(tab) <- paste(label2, c("Complete", "Partial", "Absent"),
sep=":")
}
return(tab)
}
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