updates

README.md

@@ -1 +1,74 @@
-# scRNA_shiny_app
+# scRNA-seq analysis application
+---
+# Dataset Information:
+- Dataset name: 
+- Owner: 
+- Sharing with 1 user(s) and 1 group(s) 
+- Description: 
+- File size: 
+
+---
+Shiny App Tutorial
+---
+
+### Single Marker View:
+Explore a single feature (gene, metadata, etc.) and its relation to variations of clustering or on a per sample basis. 
+
+#### Options: 
+![](./docs/single_marker.png)
+- Numeric Analysis Type: [Genes, Numeric Metadata, PCs]
+    - Genes: Are you interested in looking at genes or interest such as marker genes?
+    - Numeric Metadata: Are you interested in looking at metadata features such as: 
+        - percent mitochondria expression for each cell’s expression (percent.mito)
+        - number of total unique genes or ‘features’ expressed (nFeature_RNA)
+        - Total number of genes expressed or total count of RNA (nCount_RNA)
+    - PCs: Are you interested in exploring the principal components that contribute to the tSNE plot seen?
+- Identity: 
+    - Orig.ident: This will color the graph based on the names of the samples processed. 
+    - RNA_snn_res.0.XX: This will color the graph based on groupings produced by Seurat as various resolutions.
+        - A higher value of XX means that there is a higher resolution, and therefore more clusters or inferred groups of cell types. 
+        - A lower value of XX means that there is a 
+- Primary Numeric: This will change to be Genes, Numeric Metadata, or PCs based on the value selected for ‘Numeric Analysis Type’.
+
+#### Graphs:
+- The first plot is a tSNE that is colored based on the Primary Numeric selection. 
+- The second plot is a violin plot that displays the Identity selection on the X-axis and the Primary Numeric on the Y-axis. 
+- The third plot is the tSNE that is colored based on the Identity selection. 
+
+Having trouble understanding what a tSNE plot represents?
+- tSNE helpful video: https://www.youtube.com/watch?v=NEaUSP4YerM
+
+---
+### Double Marker View:
+Explore two features (gene, metadata, etc.) and its relation to variations of clustering or on a per sample basis. 
+
+#### Options: All of the options here are the same as the Single Marker View with the following field as an option.
+![](./docs/double_marker.png) 
+- Secondary Numeric: This, in combination with the Primary Numeric field enables a user to explore two Genes, Numeric Metadata, or PCs based on the value selected for ‘Numeric Analysis Type’.
+
+#### Graphs:
+- The first plot is a tSNE that is colored based on the Primary Numeric and Secondary Numeric selection. 
+- The second plot’s first tile is a violin plot that displays the Identity selection on the X-axis and the Primary Numeric on the Y-axis. The second plot’s second tile is the same as the first tile but is based on the selection of the Secondary Numeric field. 
+- The third plot is the tSNE that is colored based on the Identity selection. 
+
+Having trouble understanding what a tSNE plot represents?
+- tSNE helpful video: https://www.youtube.com/watch?v=NEaUSP4YerM
+
+---
+### Marker Set (Grid)
+This plot helps to explore sets of genes and their relation to the identity. 
+
+#### Options:
+![](./docs/marker_set.png)
+- Identity: the same as what is described for the Single Marker View
+- Gene Selection: here you choose the set of genes you would like to explore based on the Identity selected. 
+
+#### Graph:
+- Y-axis represents the Identity, such as the original samples or some groupings at a certain resolution.
+- X-axis represents the genes selected. (Primary Numeric) 
+- The size of each dot on the grid represents the percentage of cells that expressed that gene. 
+- The color intensity of each dot on the grid represents the average expression of the cells that expressed a given gene. 
+- So what makes for a good marker gene for some given identity?
+    - High mean expression
+    - High percentage of cells expressing the gene
+    - Low mean expression and percentage of cells expressing the gene for the rest of the identities

app.R

@@ -4,12 +4,14 @@ library(ggplot2)
 library(tidyr)
 library(dplyr)
 
-load('experiment_merged.RData')
-genes = experiment.merged@assays$RNA
-meta_nums <- colnames(dplyr::select_if(experiment.merged@meta.data, is.numeric))
-meta_cats <- colnames(dplyr::select_if(experiment.merged@meta.data, is.factor))
+
+aggregate <- readRDS('Zhang_Seurat_UCD_blood.rds')
+#aggregate
+genes = aggregate@assays$RNA
+meta_nums <- colnames(dplyr::select_if(aggregate@meta.data, is.numeric))
+meta_cats <- colnames(dplyr::select_if(aggregate@meta.data, is.factor))
 pcs <- list('PC_1','PC_2','PC_3','PC_4','PC_5','PC_6','PC_7','PC_8','PC_9')
-agg_cats <- colnames(dplyr::select_if(experiment.merged@meta.data, is.factor))
+agg_cats <- colnames(dplyr::select_if(aggregate@meta.data, is.factor))
 
 
 server = function(input, output, session){
@@ -54,48 +56,48 @@ server = function(input, output, session){
   # Marker Plot Double
   output$MarkerGenePlot <- renderPlot({
     FeaturePlot(
-      experiment.merged,
+      aggregate,
       c(input$numeric, input$numeric2), blend=TRUE
     )
   })
 
   # Marker Plot Single
   output$MarkerGenePlotSingle <- renderPlot({
     FeaturePlot(
-      experiment.merged,
+      aggregate,
       c(input$numeric_single)
     )
   })
 
   # Double Feature Categorical Feature Plot
   output$CategoricalPlot <- renderPlot({
-    DimPlot(object = experiment.merged, group.by=input$categorical, pt.size=0.5, do.label = TRUE, reduction = "tsne", label = T)
+    DimPlot(object = aggregate, group.by=input$categorical, pt.size=0.5, do.label = TRUE, reduction = "tsne", label = T)
   })
 
   # Single Feature Categorical Feature Plot
   output$CategoricalPlotSingle <- renderPlot({
-    DimPlot(object = experiment.merged, group.by=input$categorical_single, pt.size=0.5, do.label = TRUE, reduction = "tsne", label = T)
+    DimPlot(object = aggregate, group.by=input$categorical_single, pt.size=0.5, do.label = TRUE, reduction = "tsne", label = T)
   })
 
   # Double Feature Violin Plot
   output$ViolinPlot <- renderPlot({
-    Idents(experiment.merged) <- input$categorical
-    VlnPlot(object =  experiment.merged, features = c(input$numeric, input$numeric2), pt.size = 0.05)
+    Idents(aggregate) <- input$categorical
+    VlnPlot(object =  aggregate, features = c(input$numeric, input$numeric2), pt.size = 0.05)
   })
 
   # Single Feature Violin Plot
   output$ViolinPlotSingle <- renderPlot({
-    Idents(experiment.merged) <- input$categorical
-    VlnPlot(object =  experiment.merged, features = c(input$numeric_single), pt.size = 0.05)
+    Idents(aggregate) <- input$categorical_single
+    VlnPlot(object =  aggregate, features = c(input$numeric_single), pt.size = 0.05)
   })
 
   # Marker Set Plot
   output$MarkerSet <- renderPlot({
-    Idents(experiment.merged) <- input$categorical_b
+    Idents(aggregate) <- input$categorical_b
     markers = input$numeric_b
     expr.cutoff = 3
-    widedat <- FetchData(experiment.merged, markers)
-    widedat$Cluster <- Idents(experiment.merged)
+    widedat <- FetchData(aggregate, markers)
+    widedat$Cluster <- Idents(aggregate)
     longdat <- gather(widedat, key = "Gene", value = "Expression", -Cluster)
     longdat$Is.Expressed <- ifelse(longdat$Expression > expr.cutoff, 1, 0)
     longdat$Cluster <- factor(longdat$Cluster)
@@ -181,8 +183,8 @@ ui <- fluidPage(
                    tabPanel("Documentation", value=-999,
                             mainPanel(width = 12,
                               br(),
-                              includeMarkdown("docs/testing.md"),
-                              includeMarkdown("docs/todo.md")
+                              #includeMarkdown("docs/testing.md")
+                              includeMarkdown("README.md")
                             )
                    ),
 
@@ -248,4 +250,6 @@ ui <- fluidPage(
 )
 
 
-shinyApp(ui, server)
+shinyApp(ui, server)
+
+