small fixes and update CITATION.

Author: Milanez, Pedro

.gitignore

@@ -47,6 +47,8 @@ po/*~
 
 # RStudio Connect folder
 rsconnect/
+
+# RStudio Project
 tinydenseR.Rproj
 
 # macOS

R/lm.graph.embed.R

@@ -1068,12 +1068,12 @@ get.map <-
         )() |>
         unique()
 
-      .lm.obj$graph$celltyping$mean.exprs <-
+      .lm.obj$graph$celltyping$median.exprs <-
         (if(.lm.obj$assay.type == "RNA") .lm.obj$scaled.lm[,top] else .lm.obj$lm) |>
         dplyr::as_tibble() |>
         cbind(cell.pop = as.character(x = .lm.obj$graph$celltyping$ids)) |>
         dplyr::group_by(cell.pop) |>
-        dplyr::summarize_all(.funs = mean) |>
+        dplyr::summarize_all(.funs = stats::median) |>
         as.data.frame() |>
         (\(x)
          `rownames<-`(x = x[,colnames(x = x) != "cell.pop"],
@@ -1082,7 +1082,7 @@ get.map <-
         as.matrix()
 
       .lm.obj$graph$celltyping$pheatmap <-
-        pheatmap::pheatmap(mat = .lm.obj$graph$celltyping$mean.exprs,
+        pheatmap::pheatmap(mat = .lm.obj$graph$celltyping$median.exprs,
                            color = grDevices::colorRampPalette(
                              unname(obj =
                                       Color.Palette[1,c(1,6,2)]))(100),
@@ -1386,13 +1386,13 @@ lm.cluster <-
 
     }
 
-    # Compute mean expression per cluster
-    .lm.obj$graph$clustering$mean.exprs <-
+    # Compute median expression per cluster
+    .lm.obj$graph$clustering$median.exprs <-
       (if(.lm.obj$assay.type == "RNA") .lm.obj$scaled.lm[,top] else .lm.obj$lm) |>
       dplyr::as_tibble() |>
       cbind(cell.pop = as.character(x = .lm.obj$graph$clustering$ids)) |>
       dplyr::group_by(cell.pop) |>
-      dplyr::summarize_all(.funs = mean) |>
+      dplyr::summarize_all(.funs = stats::median) |>
       as.data.frame() |>
       (\(x)
        `rownames<-`(x = x[,colnames(x = x) != "cell.pop"],
@@ -1402,7 +1402,7 @@ lm.cluster <-
 
     # Generate heatmap of cluster profiles
     .lm.obj$graph$clustering$pheatmap <-
-      pheatmap::pheatmap(mat = .lm.obj$graph$clustering$mean.exprs,
+      pheatmap::pheatmap(mat = .lm.obj$graph$clustering$median.exprs,
                          color = grDevices::colorRampPalette(
                            unname(obj =
                                     Color.Palette[1,c(1,6,2)]))(100),
@@ -1411,7 +1411,7 @@ lm.cluster <-
                          border_color = NA,
                          scale = "none",
                          angle_col = 90,
-                         cluster_rows = if(nrow(x = .lm.obj$graph$clustering$mean.exprs) > 1) TRUE else FALSE,
+                         cluster_rows = if(nrow(x = .lm.obj$graph$clustering$median.exprs) > 1) TRUE else FALSE,
                          cluster_cols = TRUE,
                          cellwidth = 20,
                          cellheight = 20,

R/plot.R

@@ -570,12 +570,7 @@ plotBeeswarm <-
                          y = "",
                          size = "%") +
            ggplot2::scale_x_discrete(labels = "mean % across\nall samples") +
-           {
-             if(isFALSE(x = .order.ids)){
-               ggplot2::scale_y_discrete(limits = rev)
-             }
-               
-           } +
+           ggplot2::scale_y_discrete(limits = rev) +
            ggplot2::scale_size_continuous(range = log2(x = x$cell.perc + 1) |>
                                             range() |>
                                             I()) +
@@ -736,12 +731,7 @@ plotBeeswarm <-
                                  scales = .facet.scales)
            }
          } +
-         {
-           if(isFALSE(x = .order.ids)){
-             ggplot2::scale_y_discrete(limits = rev)
-           }
-           
-         } +
+         ggplot2::scale_y_discrete(limits = rev) +
          ggplot2::geom_point(position = ggplot2::position_jitter(width = 0,
                                                                  height = 0.25),
                              size = I(x = .point.size)) +
@@ -898,7 +888,7 @@ plot2Markers <-
 
       dat.df <-
         (.lm.obj$lm[if(!is.null(x = .id)) .id else 1:nrow(x = .lm.obj$lm),
-                    colnames(x = .lm.obj$lm) %in% c(.x.feature,.y.feature)]/50) |>
+                    colnames(x = .lm.obj$lm) %in% c(.x.feature,.y.feature)]) |>
         as.data.frame()
 
     }
@@ -936,7 +926,7 @@ plot2Markers <-
 
       p <-
         p  +
-        ggplot2::stat_density_2d(data = as.data.frame(x = .lm.obj$lm / (if(.lm.obj$assay.type == "RNA") 1 else 50)),
+        ggplot2::stat_density_2d(data = as.data.frame(x = .lm.obj$lm),
                                  mapping = ggplot2::aes(fill = ggplot2::after_stat(x = log2(x = level))),
                                  geom = "polygon",
                                  bins = .density.bins) +
@@ -1197,12 +1187,7 @@ plotTradStats <-
                        y = "",
                        size = "%") +
          ggplot2::scale_x_discrete(labels = "mean % across\nall samples") +
-         {
-           if(isFALSE(x = .order.ids)){
-             ggplot2::scale_y_discrete(limits = rev)
-           }
-           
-         } +
+         ggplot2::scale_y_discrete(limits = rev) +
          ggplot2::scale_size_continuous(range = log2(x = x$cell.perc + 1) |>
                                           range()) +
          ggplot2::geom_point() +
@@ -1296,23 +1281,13 @@ plotTradStats <-
       ggplot2::scale_fill_gradient2(low = unname(obj = Color.Palette[1,1]),
                                     mid = unname(obj = Color.Palette[1,6]),
                                     high = unname(obj = Color.Palette[1,2]),
-                                    midpoint = 0#,
-                                    #labels = ~ ifelse(test = .x >= 0,
-                                    #                  yes = round(x = 2^.x,
-                                    #                              digits = 1),
-                                    #                  no = -round(x = 1 / 2^.x,
-                                    #                              digits = 1))
+                                    midpoint = 0
       ) +
       ggplot2::scale_size_continuous(labels = ~ formatC(x = 10^(-(.x)),
                                                         format = "g",
                                                         digits = 1),
                                      range = I(x = c(3,6))) +
-      {
-        if(isFALSE(x = .order.ids)){
-          ggplot2::scale_y_discrete(limits = rev)
-        }
-        
-      } +
+      ggplot2::scale_y_discrete(limits = rev) +
       ggplot2::geom_point(shape = 21,
                           mapping = ggplot2::aes(size = -log10(x = adj.p)),
                           color = "black",
@@ -1360,10 +1335,12 @@ plotTradStats <-
 #'
 #' @param .lm.obj A tinydenseR object processed through \code{get.map()}.
 #' @param .x.split Character specifying metadata column for x-axis grouping. Defaults to first 
-#'   column (often "Condition").
+#'   column.
+#' @param .x.split.subset Optional character vector to subset \code{.x.split} categories. Default NULL.
 #' @param .pop Character vector of population names to plot. If NULL, plots all populations from 
 #'   \code{.pop.from}.
 #' @param .pop.from Character: "clustering" (default) or "celltyping" - which grouping to plot.
+#' @param .order.pop Logical whether to order populations based on dendrogram order (default FALSE).
 #' @param .line.by Character metadata column for connecting paired samples with lines (e.g., 
 #'   "Subject" for longitudinal data). Default NULL (no lines).
 #' @param .dodge.by Character metadata column for coloring/dodging points. Default NULL (all black).
@@ -1373,6 +1350,7 @@ plotTradStats <-
 #'   \code{Color.Palette[1,1:5]}).
 #' @param .seed Integer random seed for x-axis jitter (default 123).
 #' @param .orientation Character: "wide" (default, all populations in one row) or "square" (facet grid).
+#' @param .log2.y Logical whether to log2-transform y-axis percentages (default FALSE).
 #'   
 #' @return A \code{ggplot} object showing cell percentages with optional paired connections.
 #'   
@@ -1413,15 +1391,18 @@ plotTradPerc <-
   function(
     .lm.obj,
     .x.split = colnames(x = .lm.obj$metadata)[1],
+    .x.split.subset = NULL,
     .pop = NULL,
     .pop.from = "clustering",
+    .order.pop = FALSE,
     .line.by = NULL,
     .dodge.by = NULL,
     .x.space.scaler = 0.25,
     .height = 1.5,
     .cat.feature.color = Color.Palette[1,1:5],
     .seed = 123,
-    .orientation = "wide"
+    .orientation = "wide",
+    .log2.y = FALSE
   ){
 
     dodge <- value <- name <- color <- group <- x <- y <- NULL
@@ -1436,6 +1417,14 @@ plotTradPerc <-
                         collapse = ", ")))
     }
 
+    if(!is.null(x = .x.split.subset)){
+      if(!all(.x.split.subset %in% .lm.obj$metadata[[.x.split]])){
+        stop(paste0(".x.split.subset must within the following: ",
+                    paste(x = unique(x = .lm.obj$metadata[[.x.split]]),
+                          collapse = ", ")))
+      }
+    }
+    
     if(!is.null(x = .dodge.by)){
       if(length(x = .dodge.by) != 1){
         stop(".dodge.by must be length 1")
@@ -1506,6 +1495,23 @@ plotTradPerc <-
 
     }
 
+    if(!is.null(x = .x.split.subset)){
+      
+      dat.df <- 
+        droplevels(x = dat.df[dat.df$x %in% .x.split.subset,])
+      
+    }
+    
+    if(isTRUE(x = .order.pop)){
+      
+      dat.df$name <-
+        as.character(x = dat.df$name) |>
+        factor(levels = .lm.obj$graph[[.pop.from]]$pheatmap$tree_row$labels[
+          .lm.obj$graph[[.pop.from]]$pheatmap$tree_row$order
+        ])
+      
+    }
+    
     p <-
       ggplot2::ggplot(data = dat.df,
                       mapping = ggplot2::aes(x = x,
@@ -1579,6 +1585,12 @@ plotTradPerc <-
                                                                 seed = .seed))
     }
 
+    if(isTRUE(x = .log2.y)){
+      p <-
+        p +
+        ggplot2::scale_y_continuous(transform = "log2")
+    }
+    
     p
 
   }

R/stats.model.R

@@ -308,7 +308,11 @@ get.stats <-
               }
               return(adjp)
 
-            })
+            }) |>
+      (\(x)
+       `dimnames<-`(x = x,
+                    value = dimnames(x = stats$fit$p.value))
+      )()
 
     if(isTRUE(x = .verbose)){
       message("\nretrieving PCA-weighted q-values")
@@ -388,6 +392,11 @@ get.stats <-
             MARGIN = 2,
             FUN = function(pvalues){
 
+              if((is.na(x = pvalues) |> mean()) == 1){
+                return(rep(x = NA_real_,
+                           length.out = length(x = pvalues)))
+              }
+              
               if(length(x = pvalues) < 100){
 
                 res <- 

inst/CITATION

@@ -2,7 +2,7 @@ citHeader("To cite tinydenseR in publications use:")
 
 citEntry(
   entry = "Manual",
-  title = "tinydenseR: Linking Cell-To-Cell Variation to Sample-to-Sample Variation",
+  title = "Sample-level modeling of single-cell data at scale with tinydenseR",
   author = c(
     person("Pedro", "Milanez-Almeida", email = "Pedro.Milanez@Novartis.com"),
     person("Daniela", "Schildknecht"),
@@ -24,11 +24,11 @@ citEntry(
   ),
   year = "2025",
   note = "R package version 0.0.1.0008",
-  url = "https://github.com/Novartis/tinydenseR",
+  url = "https://www.biorxiv.org/content/10.1101/2025.11.26.690752v1",
+  doi = "https://doi.org/10.1101/2025.11.26.690752",
   textVersion = paste(
     "Milanez-Almeida, P. et al. (2025).",
-    "tinydenseR: Linking Cell-To-Cell Variation to Sample-to-Sample Variation.",
-    "R package version 0.0.1.0008.",
-    "URL https://github.com/Novartis/tinydenseR"
+    "Sample-level modeling of single-cell data at scale with tinydenseR.",
+    "bioRxiv https://doi.org/10.1101/2025.11.26.690752"
   )
 )