BiocCheck canges

R/corncob.R

@@ -265,11 +265,11 @@ run_corncob <- function(rec,
                   "converge!"
                 ),
                 glue::glue(
-                  "{crayon::bgMagenta('corncob')}: If you are seeing this, it ", 
-                  "is likely that your model is overspecified. This occurs ", 
+                  "{crayon::bgMagenta('corncob')}: If you are seeing this, it",
+                  " is likely that your model is overspecified. This occurs ", 
                   "when your sample size is not large enough to estimate all ", 
-                  "the parameters of your model. This is most commonly due to ", 
-                  "categorical variables that include many categories."
+                  "the parameters of your model. This is most commonly due to", 
+                  " categorical variables that include many categories."
                 ),
                 glue::glue(
                   "Please remove or edit the ", 

R/filter_taxa.R

@@ -90,10 +90,10 @@ run_filter_taxa <- function(rec, .f) {
   if (val > 0 & rm_zeros == 0 & is_metagenomeseq) {
     rlang::abort(c(
       "!" = glue::glue(
-        "{crayon::bgMagenta('step_filter_taxa()')} returns a phyloseq object ", 
-        "that contains taxa with values of 0 in all samples of a level within ",
-        "the variable of interest. This can cause errors during the execution ", 
-        "of metagenomeseq method!"
+        "{crayon::bgMagenta('step_filter_taxa()')} returns a phyloseq ", 
+        "object that contains taxa with values of 0 in all samples of a ", 
+        "level within the variable of interest. This can cause errors during ",
+        "the execution of metagenomeseq method!"
       ),
       "*" = "Please create a new recipe using a stricter filter expression.", 
       "*" = glue::glue(

R/phyloseq_qc.R

@@ -3,8 +3,8 @@
 #' phy_qc() returns a tibble. It will have information about some important
 #' metrics about the sparsity of the count matrix. The content of the table is
 #' as follows:
-#' * var_levels: levels of the categorical variable of interest. "all" refers to 
-#' all rows of the dataset (without splitting by categorical levels).
+#' * var_levels: levels of the categorical variable of interest. "all" refers 
+#' to all rows of the dataset (without splitting by categorical levels).
 #' * n: total number of values in the count matrix.
 #' * n_zero: number of zeros in the count matrix.
 #' * pct_zero: percentage of zeros in the count matrix.

R/plot_methods.R

@@ -377,8 +377,8 @@ methods::setMethod(
           "{top_n}"
         ),
         "i" = glue::glue(
-          "The top {top_n} significant taxa with the greatest overlap between ", 
-          "methods will be used"
+          "The top {top_n} significant taxa with the greatest overlap between", 
+          " methods will be used"
         )
       ))
 
@@ -395,8 +395,8 @@ methods::setMethod(
       rlang::inform(c(
         "!" = "0 taxa are present in all tested methods",
         "i" = glue::glue(
-          "The top {top_n} significant taxa with the greatest overlap between ",  
-          "methods will be used"
+          "The top {top_n} significant taxa with the greatest overlap ", 
+          "between methods will be used"
         )
       ))
 
@@ -434,7 +434,10 @@ methods::setMethod(
 #' @noRd
 #' @keywords internal
 .abundance_heatmap <- function(rec, taxa_ids, transform, scale, top_n) {
-  ComplexHeatmap::ht_opt(message = FALSE, COLUMN_ANNO_PADDING = unit(0.5, "cm"))
+  ComplexHeatmap::ht_opt(
+    message = FALSE, 
+    COLUMN_ANNO_PADDING = unit(0.5, "cm")
+  )
 
   if (is.null(taxa_ids)) {
     taxa_ids <- 
@@ -450,8 +453,8 @@ methods::setMethod(
           "{top_n}"
         ),
         "i" = glue::glue(
-          "The top {top_n} significant taxa with the greatest overlap between ", 
-          "methods will be used"
+          "The top {top_n} significant taxa with the greatest overlap between", 
+          " methods will be used"
         )
       ))
 
@@ -468,8 +471,8 @@ methods::setMethod(
       rlang::inform(c(
         "!" = "0 taxa are present in all tested methods",
         "i" = glue::glue(
-          "The top {top_n} significant taxa with the greatest overlap between ", 
-          "methods will be used"
+          "The top {top_n} significant taxa with the greatest overlap between", 
+          " methods will be used"
         )
       ))
 
@@ -630,8 +633,8 @@ methods::setMethod(
           "{top_n}"
         ),
         "i" = glue::glue(
-          "The top {top_n} significant taxa with the greatest overlap between ", 
-          "methods will be used"
+          "The top {top_n} significant taxa with the greatest overlap between", 
+          " methods will be used"
         )
       ))
 
@@ -653,8 +656,8 @@ methods::setMethod(
           "0 taxa are present with count_cutoff = {count_cutoff}"
         ),
         "i" = glue::glue(
-          "The top {top_n} significant taxa with the greatest overlap between ", 
-          "methods will be used"
+          "The top {top_n} significant taxa with the greatest overlap between", 
+          " methods will be used"
         )
       ))
 

vignettes/article.Rmd

@@ -27,15 +27,15 @@ options(digits = 3)
 ```
 
 To illustrate the functionality of the 'dar' package, this study will use the 
-data set from (Noguera-Julian, M., et al. 2016). The authors of this study found 
-that men who have sex with men (MSM) predominantly belonged to the 
+data set from (Noguera-Julian, M., et al. 2016). The authors of this study 
+found that men who have sex with men (MSM) predominantly belonged to the 
 Prevotella-rich enterotype whereas most non-MSM subjects were enriched in 
-Bacteroides, independently of HIV-1 status. This result highlights the potential 
-impact of sexual orientation on the gut microbiome and emphasizes the importance 
-of controlling for such variables in microbiome research. Using the 'dar' 
-package, we will conduct a differential abundance analysis to further explore 
-this finding and uncover potential microbial biomarkers associated with this 
-specific population.
+Bacteroides, independently of HIV-1 status. This result highlights the 
+potential impact of sexual orientation on the gut microbiome and emphasizes the 
+importance of controlling for such variables in microbiome research. Using the 
+'dar' package, we will conduct a differential abundance analysis to further 
+explore this finding and uncover potential microbial biomarkers associated with 
+this specific population.
 
 ## Load dar package and data
 
@@ -56,10 +56,10 @@ abundance analysis. The initialization of the recipe object is done through the
 function recipe(), which takes as inputs a phyloseq object, the name of the
 categorical variable of interest and the taxonomic level at which the 
 differential abundance analyses are to be performed. As previously mentioned, 
-we will use the data set from (Noguera-Julian, M., et al. 2016) and the variable 
-of interest "RiskGroup2" containing the categories: men who have sex with men 
-(msm), non-MSM (hts) and people who inject drugs (pwid) and we will perform the 
-analysis at the species level.
+we will use the data set from (Noguera-Julian, M., et al. 2016) and the 
+variable of interest "RiskGroup2" containing the categories: men who have sex 
+with men (msm), non-MSM (hts) and people who inject drugs (pwid) and we will 
+perform the analysis at the species level.
 
 ```{r}
 ## Recipe initialization
@@ -215,16 +215,19 @@ corr_heat
 ```
 
 Finally, 'dar' also includes the function mutual_plt(), which plots the number 
-of differential abundant features mutually found by a defined number of methods, 
-colored by the differential abundance direction and separated by comparison. 
-The resulting graph allows us to see that the features detected correspond 
-mainly to the comparisons between hts vs msm and msm vs pwid. Additionally, the 
-graph also allows us to observe the direction of the effect; whether a specific 
-OTU is enriched or depleted for each comparison.
+of differential abundant features mutually found by a defined number of 
+methods, colored by the differential abundance direction and separated by 
+comparison. The resulting graph allows us to see that the features detected 
+correspond mainly to the comparisons between hts vs msm and msm vs pwid. 
+Additionally, the graph also allows us to observe the direction of the effect; 
+whether a specific OTU is enriched or depleted for each comparison.
 
 ```{r, fig.height=6}
 ## Mutual plot
-mutual_plt(da_results, count_cutoff = length(steps_ids(da_results, type = "da")))
+mutual_plt(
+  da_results, 
+  count_cutoff = length(steps_ids(da_results, type = "da"))
+)
 ```
 
 ## Define a consesus strategy using bake

vignettes/dar.Rmd

@@ -124,7 +124,9 @@ full list:
 
 ```{r da_steps_list}
 grep("^step_", ls("package:dar"), value = TRUE) %>% 
-  grep("_new|_to_expr|filter|subset|rarefaction", ., value = TRUE, invert = TRUE)
+  grep(
+    "_new|_to_expr|filter|subset|rarefaction", ., value = TRUE, invert = TRUE
+  )
 ```
 
 ## Prep 

vignettes/import_export_recipes.Rmd

@@ -82,8 +82,9 @@ In this example, an empty recipe is initialized, and then the steps are
 imported from a JSON file using the `import_steps` function. The imported steps 
 are added to the existing recipe.
 
-Once the recipe is imported, we can choose to add more steps or execute the code 
-using the `prep` function. In this case, we choose to execute `prep` directly.
+Once the recipe is imported, we can choose to add more steps or execute the 
+code using the `prep` function. In this case, we choose to execute `prep` 
+directly.
 
 ```{r}
 ## Execute