Merge pull request #94 from jasenfinch/devel

v0.15.3

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: metabolyseR
 Title: Methods for Pre-Treatment, Data Mining and Correlation Analyses of Metabolomics Data
-Version: 0.15.2
+Version: 0.15.3
 Authors@R: person("Jasen", "Finch", email = "[email protected]", role = c("aut", "cre"))
 Description: A tool kit for pre-treatment, modelling, feature selection and correlation analyses of metabolomics data.
 URL: https://jasenfinch.github.io/metabolyseR/

NEWS.md

@@ -1,3 +1,9 @@
+# metabolyseR 0.15.3
+
+* Fixed the margin value displayed in plots from [`plotSupervisedRF()`](https://jasenfinch.github.io/metabolyseR/reference/plotSupervisedRF.html)
+
+* The [`plotExplanatoryHeatmap()`](https://jasenfinch.github.io/metabolyseR/reference/plotExplanatoryHeatmap.html) method for the [`Analysis`](https://jasenfinch.github.io/metabolyseR/reference/Analysis-class.html) S4 class now returns a warning and skips plotting if an error is encountered whilst trying to plot a heat map.
+
 # metabolyseR 0.15.2
 
 * Added the argument `refactor` to the method [`transformTICnorm()`](https://jasenfinch.github.io/metabolyseR/reference/transform.html) to enable the feature intensities of total ion count (TIC) normalised data to be refactored back to a range consistent with the original data by multiplying the normalised values by the median TIC.

R/plotExplanatoryHeatmap.R

@@ -475,17 +475,28 @@ setMethod('plotExplanatoryHeatmap',
                       'should be of class RandomForest or Univariate'),
                 call. = FALSE)
             }
-
+            
             x %>% 
-              map(~plotExplanatoryHeatmap(
-                .x,
-                threshold = threshold, 
-                title = response(.x),
-                distanceMeasure = distanceMeasure, 
-                clusterMethod = clusterMethod,
-                featureNames = featureNames,
-                featureLimit = featureLimit
-              ))
+              map(
+                ~{
+                  heat_map <- try(plotExplanatoryHeatmap(
+                    .x,
+                    threshold = threshold, 
+                    title = response(.x),
+                    distanceMeasure = distanceMeasure, 
+                    clusterMethod = clusterMethod,
+                    featureNames = featureNames,
+                    featureLimit = featureLimit
+                  ))
+
+                  if (!is(heat_map,'try-error')) {
+                    return(heat_map)
+                  } else {
+                    warning('Errors encounted in plotting heatmap, skipping.',call. = FALSE)
+                  }
+
+                }
+              )
           }
 )
 

R/plotSupervisedRF.R

@@ -75,7 +75,10 @@ setMethod('plotSupervisedRF',
                             labelSize = labelSize) +
                 labs(
                   caption = str_c('Margin: ',
-                                  rf@metrics$.estimate[4] %>% 
+                                  rf %>% 
+                                    metrics() %>% 
+                                    filter(.metric == 'margin') %>% 
+                                    pull(.estimate) %>% 
                                     round(3)))
 
               if (isTRUE(ROC) & rf@type == 'classification') {

tests/testthat/test-plotExplanatoryHeatmap.R

@@ -68,3 +68,32 @@ test_that('plotExplanatoryHeatmap throws error when unsupervised random forest s
     randomForest(cls = NULL)
   expect_error(plotExplanatoryHeatmap(d))
 })
+
+test_that('plotExplanatoryHeatmap throws error when unsupervised random forest supplied',{
+  p <- analysisParameters(c('pre-treatment','modelling'))
+
+  parameters(p,'pre-treatment') <- preTreatmentParameters(
+    list(
+      keep = 'classes',
+      occupancyFilter = 'maximum',
+      transform = 'TICnorm' 
+    )
+  )
+
+  changeParameter(p,'cls') <- 'day'
+  changeParameter(p,'classes') <- c('H')
+
+  p@modelling[[1]]$cls <- NULL
+  p@modelling[[1]] <- c(
+    list(
+      cls = NULL),
+    p@modelling[[1]]
+  )
+
+  analysis <- metabolyse(
+    metaboData::abr1$neg,
+    metaboData::abr1$fact,
+    p)
+
+  expect_warning(plotExplanatoryHeatmap(analysis))
+})