add corr_coef class

Correlation coefficient between features and continuous factors

Author: grlloyd

R/corr_coef_class.R

@@ -0,0 +1,101 @@
+#' correlation model class
+#'
+#' correlation model class. Calculate correlation between features and continuous variables
+#'
+#' @import struct
+#' @import stats
+#' @export corr_coef
+corr_coef<-setClass(
+    "corr_coef",
+    contains=c('method'),
+    slots=c(
+        # INPUTS
+        params.alpha='entity.stato',
+        params.mtc='entity.stato',
+        params.factor_names='entity',
+        params.method='enum',
+        # OUTPUTS
+        outputs.coeff='entity',
+        outputs.p_value='entity',
+        outputs.significant='entity'
+    ),
+    prototype = list(name='Correlation coefficient',
+        description='Calculates the correlation coefficient between features and continuous factors.',
+        type="univariate",
+        predicted='p_value',
+
+        params.factor_names=entity(name='Factor names',
+            type='character',
+            description='Names of sample_meta columns to use'
+        ),
+
+        params.alpha=entity.stato(name='Confidence level',
+            stato.id='STATO:0000053',
+            value=0.05,
+            type='numeric',
+            description='the p-value cutoff for determining significance.'
+        ),
+        params.mtc=entity.stato(name='Multiple Test Correction method',
+            stato.id='OBI:0200089',
+            value='fdr',
+            type='character',
+            description='The method used to adjust for multiple comparisons.'
+        ),
+
+        params.method=enum(name='Type of correlation',
+            value='spearman',
+            type='character',
+            description='"kendall", "pearson" or "spearman" correlation coefficient. Default="spearman".',
+            list=c("kendall", "pearson","spearman")
+        ),
+        outputs.coeff=entity(name='Correlation coefficient',
+            type='data.frame',
+            description='the value of the calculate statistics which is converted to a p-value when compared to a t-distribution.'
+        ),
+        outputs.p_value=entity.stato(name='p value',
+            stato.id='STATO:0000175',
+            type='data.frame',
+            description='the probability of observing the calculated t-statistic.'
+        ),
+
+        outputs.significant=entity(name='Significant features',
+            #stato.id='STATO:0000069',
+            type='data.frame',
+            description='TRUE if the calculated p-value is less than the supplied threhold (alpha)'
+        )
+    )
+)
+
+#' @export
+setMethod(f="method.apply",
+    signature=c("corr_coef",'dataset'),
+    definition=function(M,D)
+    {
+
+        fcn2=function(y,x) {
+            s=cor.test(x,y,method=M$method,use="na.or.complete")
+        }
+        fcn=function(x) {
+            X=D$sample_meta[M$factor_names]
+            out=unlist(lapply(X,fcn2,x))
+        }
+
+        out=apply(D$data,2,fcn)
+        out=as.data.frame(t(out))
+
+        M$p_value=data.frame('p_value'=p.adjust(out[,2],method=M$mtc),row.names = colnames(D$data))
+        M$coeff=data.frame('coeff'=out[,3],row.names = colnames(D$data))
+        M$significant=data.frame('significant'=p.adjust(out[,2],method=M$mtc)>M$alpha,row.names = colnames(D$data))
+
+        return(M)
+    }
+)
+
+
+
+
+
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