Substituting the direct embed video for a link.

README.md

@@ -1,2 +1,5 @@
 # scMonitor
 
+scMonitor is a shinny application writen in R that allows to easily explore already processed scRNAseq seurat objects.
+
+You can find a video explanation [here](https://ratg.cat/tutos/scMonitor.html).

scMonitor.eng.vtt

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+WEBVTT
+
+00:00.700 --> 00:02.600
+In this video, I will present you
+
+00:02.600 --> 00:05.500
+the shinny application scMonitor, which is
+
+00:05.500 --> 00:08.200
+oriented to easily explore
+
+00:08.200 --> 00:10.300
+a previously processed seurat object.
+
+00:11.600 --> 00:13.800
+Firstly, you should select the file
+
+00:13.800 --> 00:16.800
+containing the seurat object.
+
+00:16.800 --> 00:19.000
+For now, the file format must be RDS.
+
+00:19.700 --> 00:21.600
+Depending on the object size and
+
+00:21.600 --> 00:23.800
+computer potency the loading
+
+00:23.800 --> 00:25.000
+will take more or less time.
+
+00:26.200 --> 00:28.200
+UMAP distribution that generates
+
+00:28.400 --> 00:30.000
+is the same that shows as default,
+
+00:30.400 --> 00:33.400
+usually clusters identified by seurat itself.
+
+00:33.400 --> 00:37.200
+We can group colors by any
+
+00:37.200 --> 00:39.300
+other variable that we have included in the
+
+00:39.300 --> 00:42.600
+metadata from the object specifying it in "GroupBy".
+
+00:43.200 --> 00:45.500
+Tipically, the sample origin or
+
+00:45.500 --> 00:46.300
+a manual annotation
+
+00:46.300 --> 00:47.700
+from the cell type.
+
+00:48.800 --> 00:51.400
+Distribution can also be divided by
+
+00:51.400 --> 00:54.500
+another variable selecting in "FacetBy".
+
+00:54.500 --> 00:57.300
+In this case, we are looking at
+
+00:57.300 --> 00:59.900
+the distribution of cell type
+
+00:59.900 --> 01:01.000
+by the sample origin.
+
+01:02.800 --> 01:05.800
+Another available visualization is to show expression
+
+01:05.800 --> 01:08.100
+for a group of genes separated by a white space
+
+01:08.100 --> 01:10.400
+over UMAP distribution.
+
+01:11.300 --> 01:13.500
+With the "Height" bar we can adjust
+
+01:13.500 --> 01:15.500
+the size in order to feel comfortable.
+
+01:18.900 --> 01:22.700
+Finally, we can create signatures in order to visualize
+
+01:22.800 --> 01:25.200
+their score from cells.
+
+01:25.200 --> 01:27.300
+One must indicate the signature name and
+
+01:27.300 --> 01:29.800
+on the box below specify the genes
+
+01:29.800 --> 01:32.600
+that are included. Once added, you can
+
+01:32.600 --> 01:34.700
+specify its name in the
+
+01:34.700 --> 01:35.500
+signatures box.

scMonitor.es.vtt

@@ -0,0 +1,109 @@
+WEBVTT
+
+00:00.700 --> 00:02.600
+En este vídeo os voy a presentar
+
+00:02.600 --> 00:05.500
+la aplicación "shinny" scMonitor, que está
+
+00:05.500 --> 00:08.200
+orientada a explorar de forma sencilla un
+
+00:08.200 --> 00:10.300
+objeto seurat previamente procesado.
+
+00:11.600 --> 00:13.800
+Lo primero es seleccionar el fichero que
+
+00:13.800 --> 00:16.800
+contiene ese objeto seurat. Por ahora,
+
+00:16.800 --> 00:19.000
+el formato del fichero debe RDS.
+
+00:19.700 --> 00:21.600
+En función del tamaño del objeto y
+
+00:21.600 --> 00:23.800
+de la potencia del ordenador la carga
+
+00:23.800 --> 00:25.000
+tardará más o menos.
+
+00:26.200 --> 00:28.200
+La distribución UMAP que se genera
+
+00:28.400 --> 00:30.000
+es la que se muestra por defecto,
+
+00:30.400 --> 00:33.400
+normalmente los "clusters" identificados por el propio seurat.
+
+00:33.400 --> 00:37.200
+Podemos agrupar los colores por cualquier
+
+00:37.200 --> 00:39.300
+otra variable que hayamos incluido en los
+
+00:39.300 --> 00:42.600
+metadatos del objeto especificándolo en "GroupBy".
+
+00:43.200 --> 00:45.500
+Típicamente, el origen de la muestra o
+
+00:45.500 --> 00:46.300
+una anotación
+
+00:46.300 --> 00:47.700
+manual de tipo celular.
+
+00:48.800 --> 00:51.400
+También se puede dividir la distribución en
+
+00:51.400 --> 00:54.500
+función de una variable seleccionándolo en "FacetBy".
+
+00:54.500 --> 00:57.300
+En este caso estamos viendo la
+
+00:57.300 --> 00:59.900
+distribución de los tipos celulares en función
+
+00:59.900 --> 01:01.000
+del origen de la muestra.
+
+01:02.800 --> 01:05.800
+Otra visualización disponible es mostrar la expresión
+
+01:05.800 --> 01:08.100
+de conjuntos de genes separados por un
+
+01:08.100 --> 01:10.400
+espacio sobre la distribución UMAP.
+
+01:11.300 --> 01:13.500
+Con la barra de altura podemos ajustar
+
+01:13.500 --> 01:15.500
+el tamaño para que nos sea cómodo.
+
+01:18.900 --> 01:22.700
+Finalmente, podemos crear signaturas para luego visualizar
+
+01:22.800 --> 01:25.200
+el "score" de las células.
+
+01:25.200 --> 01:27.300
+Hay que indicar el nombre de la signatura y
+
+01:27.300 --> 01:29.800
+en la casilla inferior especificar los genes
+
+01:29.800 --> 01:32.600
+que incluye. Una vez añadida se puede
+
+01:32.600 --> 01:34.700
+especificar su nombre en la casilla de
+
+01:34.700 --> 01:35.500
+signaturas.

scMonitor/app.R

@@ -2,6 +2,7 @@ library(shiny)
 library(Seurat)
 library(shinyFiles)
 library(ggplot2)
+library(ggpubr)
 
 # Define UI for application
 ui <- fluidPage(
@@ -15,8 +16,13 @@ ui <- fluidPage(
           uiOutput('groupby'),
           uiOutput('facetby'),
           textInput('features', label = "Genes"),
+          textInput('mods', label = "Signatures"),
           actionButton("goButton", "Query"),
-          sliderInput("height", "Altura", min=1000, max=20000, step=1000, value=6000)
+          sliderInput("height", "Altura", min=1000, max=20000, step=1000, value=6000),
+          h3("Add Signature"),
+          textInput('mod_name', label = "Name"),
+          textInput('mod_feat', label = "Genes"),
+          actionButton("modButton", "Add Signature")
         ),
 
         mainPanel(
@@ -26,11 +32,15 @@ ui <- fluidPage(
     )
 )
 
-# Define server logic required to draw a histogram
+# Define server logic
 server <- function(input, output) {
+  
+  ## Defining reactive values
   dades<-reactiveValues()
   dades$seu<-NULL
+  dades$mods<-NULL
   
+  ## Conecting with file location and entering Seurat object
   volumes <- c(Home = fs::path_home(), "R Installation" = R.home(), getVolumes()())
   shinyFileChoose(input, 'file', roots=volumes)
   
@@ -40,6 +50,7 @@ server <- function(input, output) {
     }
   })
   
+  ## Rendering UI depending on seu meta.data
   output$groupby<-renderUI({
     if (!is.null(dades$seu)){
       selectInput("groupby", "GroupBy", c("Default", colnames(dades$seu@meta.data)))
@@ -52,10 +63,7 @@ server <- function(input, output) {
     }
   })
   
-  query<-eventReactive(input$goButton,{
-    dades$fet_query<-strsplit(input$features, " ")[[1]]
-  })
-  
+  ## Render DimPlot
   output$umapPlot <- renderPlot({
     observeEvent(dades$seu, {})
     if (!is.null(dades$seu)){
@@ -67,7 +75,22 @@ server <- function(input, output) {
     plot
     }
   })
-
+  
+  ## Signature - Module Definition
+  observeEvent(input$modButton, {
+    if (input$modButton > 0){
+      mod_list<-strsplit(input$mod_feat, " ")[[1]]
+      mod_list<-mod_list[mod_list %in% rownames(dades$seu)]
+      dades$seu<-AddModuleScore(dades$seu,list(mod_list),
+                                name=input$mod_name)
+    }
+  })
+  
+  ## FeaturePlot construction
+  query<-eventReactive(input$goButton,{
+    dades$fet_query<-strsplit(input$features, " ")[[1]]
+  })
+  
   output$featPlotUI<- renderUI({
     observeEvent(dades$fet_query, {})
     if (!is.null(dades$seu)){
@@ -75,17 +98,48 @@ server <- function(input, output) {
                  height = paste0(input$height/10,"px"))
     }
   })
-  
-  output$featPlot<-renderPlot({
-    observeEvent(dades$seu, {})
-    if (!is.null(dades$seu)){
-      query()
-      
-      FeaturePlot(dades$seu,
-                  split.by=if(input$facetby != "None"){input$facetby}else{NULL},
-                  features = dades$fet_query)&theme(aspect.ratio = 1)
+
+  ## ModulePlot/SigPlot construction
+  query_mod<-eventReactive(input$goButton,{
+    if (length(strsplit(input$mods, " ")[[1]]) > 0){
+      dades$mods<-paste(strsplit(input$mods, " ")[[1]], "1", sep="")
+    }else{
+      dades$mods<-NULL
     }
   })
+  
+  output$modPlotUI<- renderUI({
+    observeEvent(dades$mod_name, {})
+    if (!is.null(dades$mod_name)){
+      plotOutput("modPlot",
+                 height = paste0(input$height/10,"px"))
+    }
+  })
+  
+  ## Feature/Mod plot rendering
+  output$featPlot<-renderPlot({
+    observeEvent(input$goButton, {})
+    if (!is.null(dades$seu)){
+      query()
+      plot<-list()
+      print(dades$fet_query)
+      if (length(dades$fet_query) > 0){
+        plot[["Genes"]]<-FeaturePlot(dades$seu,
+                    split.by=if(input$facetby != "None"){input$facetby}else{NULL},
+                    features = dades$fet_query)&theme(aspect.ratio = 1)
+      }
+      query_mod()
+      print(dades$mods)
+      if (length(dades$mods) > 0){
+        plot[["Mods"]]<-FeaturePlot(dades$seu,
+                    split.by=if(input$facetby != "None"){input$facetby}else{NULL},
+                    features = dades$mods)&theme(aspect.ratio = 1)&scale_color_viridis_c()
+      }
+      do.call(ggarrange, c(plot, ncol=1))
+    }
+  })
+
+  
 }
 
 # Run the application