Remove trailing whitespaces (#588)

DESCRIPTION

@@ -2,7 +2,7 @@ Package: OMA
 Title: Orchestrating Microbiome Analysis with Bioconductor
 Version: 0.98.25
 Date: 2024-04-26
-Authors@R: 
+Authors@R:
     c(person("Leo", "Lahti", role = c("aut"),
              comment = c(ORCID = "0000-0001-5537-637X")),
       person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
@@ -11,11 +11,11 @@ Authors@R:
       person("Felix GM", "Ernst", email = "[email protected]",
              role = c("aut"),
              comment = c(ORCID = "0000-0001-5064-0928")),
-      person("and others", "(see the full list of contributors)", 
+      person("and others", "(see the full list of contributors)",
              role = c("ctb"))
          )
 Description:
-    This is a reference cookbook for **Microbiome Data Science** with 
+    This is a reference cookbook for **Microbiome Data Science** with
     R and Bioconductor.
 License: CC BY-NC-SA 4.0
 Encoding: UTF-8
@@ -59,6 +59,7 @@ Suggests:
     glmnet,
     glue,
     grid,
+    gtools,
     gsEasy,
     igraph,
     kableExtra,

inst/pages/acknowledgments.qmd

@@ -31,7 +31,7 @@ coordinated by:
    and runs regular training workshops in microbiome data science.
 
 - *Tuomas Borman*, PhD researcher and the lead developer of OMA/mia at
-   the Department of Computing, University of Turku. 
+   the Department of Computing, University of Turku.
 
 ### Contributors {-}
 
@@ -70,7 +70,7 @@ and the OMA book
 
 - *Matti Ruuskanen, PhD*, added machine learning techniques for
    microbiome analysis
-   
+
 - *Stefanie Peschel* has contributed chapters on the construction, analysis,
 and comparison of microbial association networks.
 
@@ -80,15 +80,15 @@ and a Professor for Biomedical Statistics and Data Science at
 [LMU Munich](https://www.en.statistik.uni-muenchen.de/index.html). He assisted
 in writing the chapters on network learning and comparison.
 
-- *Shigdel Rajesh, PhD* 
+- *Shigdel Rajesh, PhD*
 
-- *Artur Sannikov* 
+- *Artur Sannikov*
 
-- *Akewak Jeba* 
+- *Akewak Jeba*
 
 - *Himmi Lindgren*
 
-- *Lu Yang* 
+- *Lu Yang*
 
 - *Katariina Pärnänen*
 
@@ -114,8 +114,8 @@ in writing the chapters on network learning and comparison.
 
 -*Matteo Calgaro*
 
-- *Jacques Serizay* converted the _OMA_ book to the `BiocBook` format. This 
-allows the _OMA_ book to be built and distributed by Bioconductor. 
+- *Jacques Serizay* converted the _OMA_ book to the `BiocBook` format. This
+allows the _OMA_ book to be built and distributed by Bioconductor.
 
 - *Himel Mallick, PhD, FASA*, principal investigator and tenure-track faculty
 at Cornell University’s Department of Population Health Sciences and an adjunct
@@ -124,7 +124,7 @@ Information Science. He contributed to the chapters on meta-analyses, microbe
 set enrichment analysis (MSEA) and multi-omics prediction and classification.
 
 - *Yihan Liu*, assisted Dr. Mallick in writing the chapters on meta-anlayses,
-MSEA and multi-omics prediction and classification. 
+MSEA and multi-omics prediction and classification.
 
 ### Acknowledgments {-}
 

inst/pages/agglomeration.qmd

@@ -21,7 +21,7 @@ with counts aggregated from the lower-level taxa associated with them.
 ## Agglomerate data to certain rank {#sec-data-agglomeration}
 
 One of the main applications of taxonomic information in regards to count data
-is to agglomerate count data on taxonomic levels and track the influence of 
+is to agglomerate count data on taxonomic levels and track the influence of
 changing conditions through these levels. For this `mia` contains the
 `agglomerateByRank` function. The ideal location to store the agglomerated data
 is as an alternative experiment.
@@ -45,12 +45,12 @@ assayNames(altExp(tse, "Family"))
 ```{r}
 assay(altExp(tse, "Family"), "relabundance")[1:5, 1:7]
 ```
-  
+
 ```{r taxinfo_altexp_example}
 assay(altExp(tse, "Family"), "counts")[1:5, 1:7]
 ```
 
-`altExpNames` now consists of `Family` level data. This can be extended to use 
+`altExpNames` now consists of `Family` level data. This can be extended to use
 any taxonomic level listed in `taxonomyRanks(tse)`.
 
 We can also aggregate the data across all available ranks in one step using
@@ -102,18 +102,18 @@ dim(tse_sub)
 
 ## Agglomerate based on prevalence
 
-Rare taxa can also be aggregated into a single group "Other" instead of 
+Rare taxa can also be aggregated into a single group "Other" instead of
 filtering them out. A suitable function for this is `agglomerateByPrevalence`.
-The number of rare taxa is higher on the species level, which causes the need 
+The number of rare taxa is higher on the species level, which causes the need
 for data agglomeration by prevalence.
 
 ```{r}
 altExp(tse, "Species_byPrevalence") <- agglomerateByPrevalence(
-    tse, 
-    rank = "Species", 
-    other.label = "Other", 
-    prevalence = 5 / 100, 
-    detection = 1 / 100, 
+    tse,
+    rank = "Species",
+    other.label = "Other",
+    prevalence = 5 / 100,
+    detection = 1 / 100,
     as.relative = TRUE)
 altExp(tse, "Species_byPrevalence")
 

inst/pages/alpha_diversity.qmd

@@ -53,7 +53,7 @@ indices of dominance or rarity:
   a high share of the total species abundance in the community. Note
   that dominance indices are generally inversely correlated with other
   alpha diversity indices.
-  
+
 **Rarity** indices characterize the concentration of species at low
   abundance.  Prevalence and detection thresholds determine rare
   species whose total concentration will determine the value of a
@@ -112,11 +112,11 @@ type and final barcode).
 ```{r plot-div-obs, message=FALSE, fig.cap="Shannon diversity estimates plotted grouped by sample type with colour-labeled barcode.", cache=TRUE}
 library(scater)
 plotColData(
-    tse, 
-    "observed", 
-    "SampleType", 
+    tse,
+    "observed",
+    "SampleType",
     colour_by = "Final_Barcode") +
-    theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
+    theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
     labs(expression(Richness[Observed]))
 ```
 
@@ -187,7 +187,7 @@ plots <- lapply(
 
 # Fine-tune visual appearance
 plots <- lapply(
-    plots, "+", 
+    plots, "+",
     theme(axis.text.x = element_blank(),
           axis.title.x = element_blank(),
           axis.ticks.x = element_blank()))
@@ -197,7 +197,7 @@ wrap_plots(plots, ncol = 1) +
   plot_layout(guides = "collect")
 ```
 
-## Visualizing significance in group-wise comparisons 
+## Visualizing significance in group-wise comparisons
 
 Next, let's compare the Shannon index between sample groups and visualize the
 statistical significance. Using the `stat_compare_means` function from the
@@ -233,8 +233,8 @@ pvals <- reshape(
     pvals,
     direction = "long",
     varying = colnames(pvals)[ !colnames(pvals) %in% varname ],
-    times = colnames(pvals)[ !colnames(pvals) %in% varname ], 
-    v.names = "p", 
+    times = colnames(pvals)[ !colnames(pvals) %in% varname ],
+    v.names = "p",
     timevar = "group2",
     idvar = "group1"
     ) |>