Update examples 2- IL

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: OMA
 Title: Orchestrating Microbiome Analysis with Bioconductor
-Version: 0.98.26
+Version: 0.98.27
 Date: 2024-10-01
 Authors@R:
     c(person("Leo", "Lahti", role = c("aut"),
@@ -62,6 +62,7 @@ Suggests:
     gtools,
     gsEasy,
     igraph,
+    IntegratedLearner,
     knitr,
     Maaslin2,
     mia,
@@ -97,7 +98,6 @@ Suggests:
     SPRING,
     stats,
     stringr,
-    SuperLearner,
     tidyverse,
     topGO,
     vegan,
@@ -109,7 +109,8 @@ Remotes:
     github::microbiome/miaTime,
     github::stefpeschel/NetCoMi,
     github::zdk123/SpiecEasi,
-    github::GraceYoon/SPRING
+    github::GraceYoon/SPRING,
+    github::himelmallick/IntegratedLearner
 VignetteBuilder: knitr
 RoxygenNote: 7.3.1
 BiocType: Book

inst/pages/cross_correlation.qmd

@@ -48,15 +48,6 @@ data(HintikkaXOData, package = "mia")
 mae <- HintikkaXOData
 ```
 
-```{r cross-association2}
-library(stringr)
-# Drop those bacteria that do not include information in Phylum or lower levels
-mae[[1]] <- mae[[1]][!is.na(rowData(mae[[1]])$Phylum), ]
-# Clean taxonomy data, so that names do not include additional characters
-rowData(mae[[1]]) <- DataFrame(
-    apply(rowData(mae[[1]]), 2, str_remove, pattern = "._[0-9]__"))
-```
-
 ```{r}
 # Available alternative experiments
 experiments(mae)
@@ -84,7 +75,7 @@ bacterium X is present, is the concentration of metabolite Y lower or higher"?
 
 ```{r cross-correlation5}
 # Agglomerate microbiome data at family level
-mae[[1]] <- agglomerateByPrevalence(mae[[1]], rank = "Family")
+mae[[1]] <- agglomerateByPrevalence(mae[[1]], rank = "Family", na.rm = TRUE)
 # Does log10 transform for microbiome data
 mae[[1]] <- transformAssay(mae[[1]], method = "log10", pseudocount = TRUE)