Ensured same code writing style across code blocks and vignettes.

vignettes/decoupleR.Rmd

@@ -179,6 +179,7 @@ res_gsea <- decoupleR::run_fgsea(mat = mat,
                                  .target = 'target', 
                                  nproc = 1, 
                                  minsize = 0)
+
 res_gsea
 ```
 
@@ -202,6 +203,7 @@ res_ulm <- decoupleR::run_ulm(mat = mat,
                               .target = 'target', 
                               .mor = 'mor', 
                               minsize = 0)
+
 res_ulm
 ```
 
@@ -285,6 +287,7 @@ res_decouple <- decoupleR::decouple(mat,
                                     .source ='source', 
                                     .target ='target',
                                     minsize = 0)
+
 res_decouple
 ```
 

vignettes/pw_bk.Rmd

@@ -86,6 +86,7 @@ head(counts)
 The design meta-data:
 ```{r "design"}
 design <- data$design
+
 design
 ```
 
@@ -197,6 +198,7 @@ contrast_acts <- decoupleR::run_mlm(mat  =deg,
                                     .target = 'target',
                                     .mor = 'weight', 
                                     minsize = 5)
+
 contrast_acts
 ```
 

vignettes/tf_bk.Rmd

@@ -79,12 +79,14 @@ counts <- data$counts %>%
                            ~ dplyr::if_else(is.na(.x), 0, .x)) %>% 
           tibble::column_to_rownames(var = "gene") %>% 
           as.matrix()
+
 head(counts)
 ```
 
 The design meta-data:
 ```{r "design"}
 design <- data$design
+
 design
 ```
 
@@ -97,6 +99,7 @@ deg <- data$limma_ttop %>%
        dplyr::filter(!is.na(t)) %>% 
        tibble::column_to_rownames(var = "ID") %>%
        as.matrix()
+
 head(deg)
 ```
 
@@ -118,6 +121,7 @@ recommend to keep complexes together.
 ```{r "collectri"}
 net <- decoupleR::get_collectri(organism = 'human', 
                                 split_complexes = FALSE)
+
 net
 ```
 
@@ -140,6 +144,7 @@ sample_acts <- decoupleR::run_ulm(mat = counts,
                                   .target = 'target',
                                   .mor = 'mor', 
                                   minsize = 5)
+
 sample_acts
 ```
 
@@ -199,6 +204,7 @@ contrast_acts <- decoupleR::run_ulm(mat = deg[, 't', drop = FALSE],
                                     .target = 'target',
                                     .mor='mor', 
                                     minsize = 5)
+
 contrast_acts
 ```
 
@@ -247,6 +253,7 @@ p <- ggplot2::ggplot(data = f_contrast_acts,
               panel.grid.major = element_blank(), 
               panel.grid.minor = element_blank()) +
      ggplot2::xlab("TFs")
+
 p
 ```
 
@@ -295,6 +302,7 @@ p <- ggplot2::ggplot(data = df,
      ggplot2::geom_vline(xintercept = 0, linetype = 'dotted') +
      ggplot2::geom_hline(yintercept = 0, linetype = 'dotted') +
      ggplot2::ggtitle(tf)
+
 p
 ```
 

vignettes/tf_sc.Rmd

@@ -69,6 +69,7 @@ p <- Seurat::DimPlot(data,
                      label = TRUE, 
                      pt.size = 0.5) + 
      Seurat::NoLegend()
+
 p
 ```
 
@@ -90,6 +91,7 @@ recommend to keep complexes together.
 ```{r "collectri"}
 net <- decoupleR::get_collectri(organism = 'human', 
                                 split_complexes = FALSE)
+
 net
 ```
 
@@ -115,6 +117,7 @@ acts <- decoupleR::run_ulm(mat = mat,
                            .target = 'target',
                            .mor='mor', 
                            minsize = 5)
+
 acts
 ```
 
@@ -168,14 +171,17 @@ p3 <- Seurat::FeaturePlot(data,
       ggplot2::ggtitle('PAX5 expression')
 
 Seurat::DefaultAssay(data) <- "tfsulm"
+
 p <- p1 | p2 | p3
+p
 ```
 
 # Exploration
 We can also see what is the mean activity per group of the top 20 more variable
 TFs:
 ```{r "mean_acts", message = FALSE, warning = FALSE}
 n_tfs <- 25
+
 # Extract activities from object as a long dataframe
 df <- t(as.matrix(data@assays$tfsulm@data)) %>%
       as.data.frame() %>%