Merge branch 'release/v2.0.12' into main

Author: dputhier

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: scigenex
 Type: Package
 Title: The scigenex package (Single-Cell Informative GENe Explorer)
-Version: 2.0.11
+Version: 2.0.12
 Date: 2020-07-22
 Author: J. Bavais, Sebastien Nin, Lionel Spinelli and Denis Puthier
 Maintainer: J. Bavais <julie.bavais@yahoo.fr>

Makefile

@@ -1,5 +1,5 @@
 MAKEFILE=Makefile
-VERSION=2.0.11
+VERSION=2.0.12
 
 .PHONY: help
 

R/gm_report.R

@@ -35,6 +35,7 @@
 #' @param FeaturePlot_params Some parameters for Seurat::FeaturePlot() function.
 #' @param SpatialFeaturePlot_params Some parameters for Seurat::SpatialFeaturePlot() function.
 #' @param SpatialDimPlot_params Some parameters for Seurat::SpatialDimPlot() function.
+#' @param DimPlot_params Some parameters for Seurat::DimPlot() function.
 #' @param plot_ggheatmap_params Some parameters for plot_ggheatmap() function.
 #' @param subsample_by_ident_params The number of cell to take per cell type when subsampling the data for plotting interactive heatmap.
 #' Reduce this number if you have a large dataset (e.g. > 10000 cells/spots). Otherwise the dataset will be too large to be handled by the web browser.
@@ -145,13 +146,15 @@ gm_report <- function(cluster_set = NULL,
                                                          name = "MOD_",
                                                          slot = "data"),
                             plot_profiles_params=list(to_lin=TRUE, averaged = TRUE),
-                            plot_multi_profiles_params=list(legend_name="Gene\nModule"),
+                            plot_multi_profiles_params=list(legend_name="Gene\nModule",
+                                                            center=FALSE),
                             FeaturePlot_params=list(cols=RColorBrewer::brewer.pal(3, "BuPu")),
                             SpatialFeaturePlot_params=list(pt.size.factor = 1.7),
-                            SpatialDimPlot_params=list(pt.size.factor = 1.7, 
-                                                       label = T, 
-                                                       repel = T, 
-                                                       label.size = 4),
+                            SpatialDimPlot_params=list(pt.size.factor = 1.7),
+                            DimPlot_params=list(label = TRUE,  
+                                                label.size = 4, 
+                                                repel = TRUE,
+                                                split.by = "orig.ident"),
                             plot_ggheatmap_params=list(use_top_genes=FALSE, 
                                                        hide_gene_name=TRUE,
                                                        xlab = "Cells/Spots",

R/plot_profiles.R

@@ -103,8 +103,8 @@ plot_profiles <- function(data = NULL,
 
   colnames(m) <- c("Cluster", "Cell", "Intensity")
   m$Cluster <- factor(
-    paste0("Cluster: ", m$Cluster),
-    levels = paste0("Cluster: ", unique(m$Cluster)),
+    paste0("Module: ", m$Cluster),
+    levels = paste0("Module: ", unique(m$Cluster)),
     ordered = T
   )
 
@@ -141,7 +141,7 @@ plot_profiles <- function(data = NULL,
   }
 
 
-  df_text$Cluster <- factor(paste0("Cluster: ", 
+  df_text$Cluster <- factor(paste0("Module: ", 
                                    rownames(centers)),
                             ordered = T)
 

R/visualization.R

@@ -728,7 +728,7 @@ setMethod(
     p <- p + ggplot2::theme_bw()
     p <- p + ggplot2::scale_fill_gradientn(
       colours = color.ramp,
-      name = "Signal"
+      name = "Scaled\nsignal"
     )
 
     nb_cell_classes <- length(table(m_melt$cell_clusters))

changelog.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## v2.0.12
+
+* Various minor modif including split.by in Dimplot
+
 ## v2.0.11
 
 * Added sample_gg_heatmap arg to gm_report

inst/rmarkdown/index.Rmd

@@ -147,7 +147,7 @@ knitr::asis_output("A 2D plot showing the first and second components of the PCA
 ``` 
 
 ```{r section_gen_info_14c, echo=FALSE, eval="exp_pca" %in% section, echo=FALSE, results = "asis"}
-Seurat::DimPlot(seurat_object, reduction = "pca")
+Seurat::DimPlot(seurat_object, reduction = "pca", label = TRUE,  label.size = 4, repel = TRUE)
 ```
 
 ```{r, section_gen_info_14d, eval="exp_elbow" %in% section, echo=FALSE, results = "asis"} 
@@ -171,10 +171,32 @@ knitr::asis_output("A UMAP showing the cell/spot classes.")
 ``` 
 
 ```{r section_gen_info_17, echo=FALSE, eval="exp_umap" %in% section, echo=FALSE, results = "asis"}
-Seurat::DimPlot(seurat_object)
+DimPlot_used_params <- as.list(formals(Seurat::DimPlot))
+for(i in names(DimPlot_params)){
+  DimPlot_used_params[[i]] <- DimPlot_params[[i]]}
+DimPlot_used_params$object <- seurat_object
+DimPlot_used_params$split.by <- NULL
+DimPlot_used_params$group.by <- NULL
+print(base::do.call(Seurat::DimPlot, DimPlot_used_params))
 ```
 
 
+```{r, section_gen_info_15_1, eval="exp_umap" %in% section, echo=FALSE, results = "asis"} 
+knitr::asis_output("### Dimplot (by origin)") 
+``` 
+
+```{r, section_gen_info_16_1, eval="exp_umap" %in% section, echo=FALSE, results = "asis"} 
+knitr::asis_output("A UMAP showing the cell/spot classes according to origin.") 
+``` 
+
+```{r section_gen_info_17_1, echo=FALSE, eval="exp_umap" %in% section, echo=FALSE, results = "asis"}
+DimPlot_used_params <- as.list(formals(Seurat::DimPlot))
+for(i in names(DimPlot_params)){
+  DimPlot_used_params[[i]] <- DimPlot_params[[i]]}
+DimPlot_used_params$object <- seurat_object
+print(base::do.call(Seurat::DimPlot, DimPlot_used_params))
+```
+
 
 ```{r, section_gen_info_17a, eval="exp_metrics" %in% section, echo=FALSE, results = "asis"} 
 knitr::asis_output("### QC Metrics") 

inst/rmarkdown/module.Rmd

@@ -184,18 +184,18 @@ knitr::asis_output("What Gemini tells us about the likely cell identity or under
 ``` 
 
 ```{r, cell_annot_IA_3_MOD_MODULE_NUMBER, echo=FALSE, eval="module_cell_annot_IA" %in% section }
-prompt <- paste0("\n\nGiven that you are a biologist working with ", smp_region, " cell compartment from ", smp_organ, " at ",  smp_stage , " stage. ",
+prompt <- paste0("\n\nGiven a biological sample from ", smp_region, " cell compartment from ", smp_organ, " at ",  smp_stage , " stage. ",
                   "This biological sample was analyzed using transcriptomic analysis.",
                   " You found a list of co-regulated genes that contains :",
-                  paste0(unlist(cluster_set[MODULE_NUMBER,]@gene_clusters), collapse=", "),
+                  paste0(sort(unlist(cluster_set[MODULE_NUMBER,]@gene_clusters)), collapse=", "),
                   ". What could be the cells, functions or pathways most likely related to theses genes ? The answers (n=4 in total) should be ordered by likelyhood and formatted as a list with the following item : 'infered cell type/function/pathway' (#1, #2, #3, #4) and 'rational'\n\n.", collapse=" ")
 
 knitr::asis_output("#### Prompt")
 
 knitr::asis_output(prompt)
-
+cat("\n\n")
 knitr::asis_output("#### IA Answer")
-
+cat("\n\n")
 suppressMessages(gemini.R::setAPI("APIKEY"))
 answer <- NULL
 try_err <- try(suppressMessages(gemini.R::gemini_chat(prompt)))
@@ -204,7 +204,7 @@ if(inherits(try_err, "try-error")){
    cat("\n\nSeems we have an issue with Gemini...\n\n") 
 }else{
   if(!is.null(try_err) )
-    knitr::asis_output(try_err$outputs )
+    knitr::asis_output(paste0("\n\n", try_err$outputs, "\n\n"))
 }
 
 cat("\n\n")