add additional checks to ttest for paired samples

Author: grlloyd

R/ttest_class.R

@@ -97,30 +97,54 @@ setMethod(f="method.apply",
             u=unique(D$sample_meta[[M$paired_factor]])
             out=character(0) # list of sample_id to remove
             for (k in u) {
-                n=sum(D$sample_meta[[M$paired_factor]]==k)
+                n=sum(D$sample_meta[[M$paired_factor]]==k) # number of samples (could be same class)
                 if (n<2) {
                     out=c(out,k)
                 }
+                # if we have more than 2 then we need an even number.
+                if (n%%2 != 0) {
+                    out=c(out,k)
+                }
+                # check we have enough groups (must be two for ttest)
+                ng=length(unique(D$sample_meta[[M$factor_names]][D$sample_meta[[M$paired_factor]]==k]))
+                if (ng != 2) {
+                    out=c(out,k)
+                }
 
             }
             X=X[!(D$sample_meta[[M$paired_factor]] %in% out),]
             y=y[!(D$sample_meta[[M$paired_factor]] %in% out)]
 
-          # sort the data by sample id so that theyre in the right order for paired ttest
-          X=apply(X,2,function(x) {
-            a=x[y==L[1]]
-            b=x[y==L[2]]
-            ay=y[y==L[1]]
-            by=y[y==L[2]]
-            a=a[order(ay)]
-            b=b[order(by)]
-            return(c(a,b))
-          })
-          # also make sure the meta data is in the right order
-          y=y[order(D$sample_meta[[M$paired_factor]])]
+            # sort the data by sample id so that theyre in the right order for paired ttest
+            X=apply(X,2,function(x) {
+                a=x[y==L[1]]
+                b=x[y==L[2]]
+                ay=y[y==L[1]]
+                by=y[y==L[2]]
+                a=a[order(ay)]
+                b=b[order(by)]
+                return(c(a,b))
+            })
+            # also make sure the meta data is in the right order
+            y=y[order(D$sample_meta[[M$paired_factor]])]
+
+            # put back into dataset object
+            D$data=as.data.frame(X)
+            D$sample_meta[[M$paired_factor]]=y
         }
 
-        output=apply(X,2,function(x) {a=unlist(t.test(x[y==L[1]],x[y==L[2]],paired = M$paired)[c("statistic","p.value","parameter",'conf.int','estimate')])})
+        # check number per class
+        # if less then 2 then remove
+        FF=filter_na_count(threshold=2,factor_name=M$factor_names)
+        FF=method.apply(FF,D)
+        D=predicted(FF)
+        X=D$data
+        y=D$sample_meta[[M$paired_factor]]
+        print(FF$count)
+
+        output=apply(X,2,function(x) {
+            a=unlist(t.test(x[y==L[1]],x[y==L[2]],paired = M$paired)[c("statistic","p.value","parameter",'conf.int','estimate')])
+        })
         output=as.data.frame(t(output),stringsAsFactors = FALSE)
         output$p.value=p.adjust(output$p.value,method = param.value(M,'mtc'))
         output.value(M,'t_statistic')=output$statistic.t