1. scone
  2. Commit d415a9d80dab83379564b1a56b20e8c144388729
Browse code

Shiny app packages to Suggests:

Davide Risso authored on 30/06/2017 21:47:09
Showing 4 changed files
DESCRIPTION
History View file @ d415a9d
... ... 
@@ -39,21 +39,21 @@ Imports:
39 39 
   boot,
40 40 
   rhdf5,
41 41 
   RUVSeq,
42 
-  DT,
43 
-  NMF,
44 
-  ggplot2,
45 
-  plotly,
46 
-  reshape2,
47 
-  visNetwork,
48 42 
   rARPACK
49 43 
 Suggests:
50 44 
   BiocStyle,
45 
+  DT,
46 
+  ggplot2,
51 47 
   knitr,
52 48 
   miniUI,
49 
+  NMF,
50 
+  plotly,
51 
+  reshape2,
53 52 
   rmarkdown,
54 53 
   scRNAseq,
55 54 
   shiny,
56 
-  testthat
55 
+  testthat,
56 
+  visNetwork
57 57 
 VignetteBuilder: knitr
58 58 
 LazyLoad: yes
59 59 
 biocViews:
NAMESPACE
History View file @ d415a9d
... ... 
@@ -42,15 +42,7 @@ import(BiocParallel)
42 42 
 import(SummarizedExperiment)
43 43 
 import(gplots)
44 44 
 import(methods)
45 
-import(plotly)
46 
-import(visNetwork)
47 45 
 importClassesFrom(SummarizedExperiment,SummarizedExperiment)
48 
-importFrom(DT,dataTableOutput)
49 
-importFrom(DT,dataTableProxy)
50 
-importFrom(DT,formatSignif)
51 
-importFrom(DT,renderDataTable)
52 
-importFrom(DT,selectRows)
53 
-importFrom(NMF,aheatmap)
54 46 
 importFrom(RColorBrewer,brewer.pal)
55 47 
 importFrom(RUVSeq,RUVg)
56 48 
 importFrom(aroma.light,normalizeQuantileRank.matrix)
... ... 
@@ -62,18 +54,6 @@ importFrom(compositions,clr)
62 54 
 importFrom(diptest,dip.test)
63 55 
 importFrom(edgeR,calcNormFactors)
64 56 
 importFrom(fpc,pamk)
65 
-importFrom(ggplot2,aes)
66 
-importFrom(ggplot2,coord_cartesian)
67 
-importFrom(ggplot2,element_blank)
68 
-importFrom(ggplot2,geom_bar)
69 
-importFrom(ggplot2,geom_point)
70 
-importFrom(ggplot2,geom_violin)
71 
-importFrom(ggplot2,ggplot)
72 
-importFrom(ggplot2,guides)
73 
-importFrom(ggplot2,labs)
74 
-importFrom(ggplot2,scale_fill_manual)
75 
-importFrom(ggplot2,theme)
76 
-importFrom(ggplot2,ylim)
77 57 
 importFrom(grDevices,colorRampPalette)
78 58 
 importFrom(grDevices,dev.off)
79 59 
 importFrom(grDevices,pdf)
... ... 
@@ -95,7 +75,6 @@ importFrom(matrixStats,colMedians)
95 75 
 importFrom(matrixStats,rowMedians)
96 76 
 importFrom(mixtools,normalmixEM)
97 77 
 importFrom(rARPACK,svds)
98 
-importFrom(reshape2,melt)
99 78 
 importFrom(rhdf5,H5close)
100 79 
 importFrom(rhdf5,h5createFile)
101 80 
 importFrom(rhdf5,h5ls)
... ... 
@@ -103,32 +82,6 @@ importFrom(rhdf5,h5read)
103 82 
 importFrom(rhdf5,h5write)
104 83 
 importFrom(rhdf5,h5write.default)
105 84 
 importFrom(scran,computeSumFactors)
106 
-importFrom(shiny,br)
107 
-importFrom(shiny,column)
108 
-importFrom(shiny,downloadHandler)
109 
-importFrom(shiny,downloadLink)
110 
-importFrom(shiny,fluidPage)
111 
-importFrom(shiny,fluidRow)
112 
-importFrom(shiny,h5)
113 
-importFrom(shiny,h6)
114 
-importFrom(shiny,helpText)
115 
-importFrom(shiny,mainPanel)
116 
-importFrom(shiny,observeEvent)
117 
-importFrom(shiny,p)
118 
-importFrom(shiny,plotOutput)
119 
-importFrom(shiny,reactive)
120 
-importFrom(shiny,renderPlot)
121 
-importFrom(shiny,renderTable)
122 
-importFrom(shiny,selectInput)
123 
-importFrom(shiny,shinyApp)
124 
-importFrom(shiny,sidebarLayout)
125 
-importFrom(shiny,sidebarPanel)
126 
-importFrom(shiny,sliderInput)
127 
-importFrom(shiny,tabPanel)
128 
-importFrom(shiny,tableOutput)
129 
-importFrom(shiny,tabsetPanel)
130 
-importFrom(shiny,titlePanel)
131 
-importFrom(shiny,updateSelectInput)
132 85 
 importFrom(stats,approx)
133 86 
 importFrom(stats,as.formula)
134 87 
 importFrom(stats,binomial)
R/sconeReport.R
History View file @ d415a9d
... ... 
@@ -1,8 +1,8 @@
1 1 
 #' SCONE Report Browser: Browse Evaluation of Normalization Performance
2 
-#'
3 
-#' This function opens a shiny application session for visualizing performance
2 
+#'
3 
+#' This function opens a shiny application session for visualizing performance
4 4 
 #' of a variety of normalization schemes.
5 
-#'
5 
+#'
6 6 
 #' @param x a \code{SconeExperiment} object
7 7 
 #' @param methods character specifying the normalizations to report.
8 8 
 #' @param qc matrix. QC metrics to be used for QC evaluation report. Required.
... ... 
@@ -10,43 +10,31 @@
10 10 
 #'   Default NULL.
11 11 
 #' @param batch factor. A known batch variable (variation to be removed).
12 12 
 #'   Default NULL.
13 
-#' @param negcon character. Genes to be used as negative controls for
14 
-#'   evaluation. These genes should be expected not to change according to the
13 
+#' @param negcon character. Genes to be used as negative controls for
14 
+#'   evaluation. These genes should be expected not to change according to the
15 15 
 #'   biological phenomenon of interest. Default empty character.
16 
-#' @param poscon character. Genes to be used as positive controls for
17 
-#'   evaluation. These genes should be expected to change according to the
16 
+#' @param poscon character. Genes to be used as positive controls for
17 
+#'   evaluation. These genes should be expected to change according to the
18 18 
 #'   biological phenomenon of interest. Default empty character.
19 
-#' @param eval_proj function. Projection function for evaluation  (see
20 
-#'   \code{\link{score_matrix}} for details). If NULL, PCA is used for
19 
+#' @param eval_proj function. Projection function for evaluation  (see
20 
+#'   \code{\link{score_matrix}} for details). If NULL, PCA is used for
21 21 
 #'   projection.
22 22 
 #' @param eval_proj_args list. List of args passed to projection function as
23 23 
 #'   eval_proj_args.
24 
-#'
25 
-#' @importFrom shiny fluidPage downloadLink br titlePanel tableOutput helpText
26 
-#'   downloadHandler column h5 h6 updateSelectInput plotOutput tabsetPanel
27 
-#'   selectInput renderTable p sidebarLayout sidebarPanel shinyApp mainPanel
28 
-#'   observeEvent renderPlot reactive fluidRow sliderInput tabPanel
29 
-#' @import visNetwork
30 
-#' @importFrom DT dataTableProxy dataTableOutput renderDataTable selectRows
31 
-#'   formatSignif
32 
-#' @import plotly
24 
+#'
33 25 
 #' @importFrom RColorBrewer brewer.pal
34 
-#' @importFrom reshape2 melt
35 
-#' @importFrom NMF aheatmap
36 
-#' @importFrom ggplot2 labs theme geom_point ylim ggplot geom_violin
37 
-#'   element_blank aes geom_bar coord_cartesian scale_fill_manual guides
38 26 
 #' @importFrom rARPACK svds
39 27 
 #' @export
40 
-#'
28 
+#'
41 29 
 #' @return An object that represents the SCONE report app.
42 
-#'
30 
+#'
43 31 
 #' @examples
44 32 
 #' set.seed(101)
45 33 
 #' mat <- matrix(rpois(1000, lambda = 5), ncol=10)
46 34 
 #' colnames(mat) <- paste("X", 1:ncol(mat), sep="")
47 35 
 #' obj <- SconeExperiment(mat)
48 36 
 #' res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
49 
-#'            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,
37 
+#'            evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,
50 38 
 #'            bpparam = BiocParallel::SerialParam())
51 39 
 #' qc = as.matrix(cbind(colSums(mat),colSums(mat > 0)))
52 40 
 #' rownames(qc) = colnames(mat)
... ... 
@@ -54,47 +42,74 @@
54 42 
 #' \dontrun{
55 43 
 #' sconeReport(res,rownames(get_params(res)), qc = qc)
56 44 
 #' }
57 
-#'
45 
+#'
58 46 
 sconeReport = function(x, methods,
59 
-                       qc,
47 
+                       qc,
60 48 
                        bio = NULL, batch = NULL,
61 49 
                        poscon = character(), negcon = character(),
62 50 
                        eval_proj = NULL,
63 51 
                        eval_proj_args = NULL){
64 
-
65 
-
52 
+
53 
+  if (!requireNamespace("shiny", quietly = TRUE)) {
54 
+    stop("shiny package needed for sconeReport()")
55 
+  }
56 
+
57 
+  if (!requireNamespace("DT", quietly = TRUE)) {
58 
+    stop("DT package needed for sconeReport()")
59 
+  }
60 
+
61 
+  if (!requireNamespace("NMF", quietly = TRUE)) {
62 
+    stop("NMF package needed for sconeReport()")
63 
+  }
64 
+
65 
+  if (!requireNamespace("reshape2", quietly = TRUE)) {
66 
+    stop("reshape2 package needed for sconeReport()")
67 
+  }
68 
+
69 
+  if (!require("plotly", quietly = TRUE)) {
70 
+    stop("plotly package needed for sconeReport()")
71 
+  }
72 
+
73 
+  if (!require("visNetwork", quietly = TRUE)) {
74 
+    stop("visNetwork package needed for sconeReport()")
75 
+  }
76 
+
77 
+  if (!require("ggplot2", quietly = TRUE)) {
78 
+    stop("ggplot2 package needed for sconeReport()")
79 
+  }
80 
+
66 81 
   scone_res = list()
67 
-
82 
+
68 83 
   # List of normalized matrices
69 
-  scone_res$normalized_data = lapply(as.list(methods),
84 
+  scone_res$normalized_data = lapply(as.list(methods),
70 85 
                                      FUN = function(z){
71 86 
                                        get_normalized(x,method = z,log=TRUE)
72 87 
                                      })
73 88 
   names(scone_res$normalized_data) = methods
74 
-
89 
+
75 90 
   # Parameter matrix
76 91 
   scone_res$params = get_params(x)[methods,]
77 
-
92 
+
78 93 
   # Merged score matrix
79 94 
   scone_res$scores = cbind(get_scores(x),get_score_ranks(x))[methods,]
80 95 
   colnames(scone_res$scores)[ncol(scone_res$scores)] = "mean_score_rank"
81 
-
82 
-
96 
+
97 
+
83 98 
   ## ----- If NULL classifications, Replace with NA ------
84 
-
99 
+
85 100 
   if(is.null(bio)){
86 101 
     bio = factor(rep("NA",ncol(scone_res$normalized_data[[1]])))
87 102 
   }
88 
-
103 
+
89 104 
   if(is.null(batch)){
90 105 
     batch = factor(rep("NA",ncol(scone_res$normalized_data[[1]])))
91 106 
   }
92 
-
107 
+
93 108 
   ## ----- Network Data for Visualization -----
94 
-
109 
+
95 110 
   # Matrix nodes in scone_res
96 
-  leaves = rownames(scone_res$params)
97 
-
111 
+  leaves = rownames(scone_res$params)
112 
+
98 113 
   # Parents
99 114 
   lay1 = unique(gsub(",[^,]*$","",leaves))
100 115 
   lay2 = unique(gsub(",[^,]*$","",lay1))
... ... 
@@ -102,37 +117,37 @@ sconeReport = function(x, methods,
102 117 
   lay4 = unique(gsub(",[^,]*$","",lay3))
103 118 
   all_nodes = c(leaves,lay1,lay2,lay3,lay4)
104 119 
   rm(list = c("lay1","lay2","lay3","lay4"))
105 
-
120 
+
106 121 
   # Collapse no-op names for all node names
107 122 
   all_nodes = unique(gsub("^,*|,*$","",
108 123 
                           gsub("\\,+",",",
109 124 
                                gsub("none|no_uv|no_bio|no_batch","",
110 125 
                                     all_nodes))))
111 
-
126 
+
112 127 
   # Order by decreasing number of operations (root at the end)
113 128 
   all_nodes = all_nodes[order(paste0(gsub(".+",",",all_nodes),
114 129 
                                      gsub("[^,]*","",all_nodes)),
115 130 
                               decreasing = TRUE)]
116 
-
131 
+
117 132 
   # Collapse no-op names for all leaves
118 133 
   leaves = gsub("^,*|,*$","",
119 134 
                 gsub("\\,+",",",
120 135 
                      gsub("none|no_uv|no_bio|no_batch","",
121 136 
                           leaves)))
122 
-
137 
+
123 138 
   # Re-title nodes in scone_res and root (instead of collapsed names)
124 139 
   titles = all_nodes
125 140 
   names(titles) =  paste0("_",all_nodes)
126 141 
   titles[paste0("_",leaves)] = rownames(scone_res$params)
127 142 
   titles[titles == ""] = "none"
128 
-
143 
+
129 144 
   # Create Nodes
130 145 
   nodes <- data.frame(id = 1:length(all_nodes),
131 146 
                       title = titles,
132 147 
                       group = c("NoData",
133 
-                                "Loaded")[1 +
148 
+                                "Loaded")[1 +
134 149 
                                             (all_nodes  %in% leaves)])
135 
-
150 
+
136 151 
   # Create Edges
137 152 
   edges = data.frame()
138 153 
   for(i in 1:length(all_nodes)){
... ... 
@@ -146,7 +161,7 @@ sconeReport = function(x, methods,
146 161 
     }
147 162 
   }
148 163 
   colnames(edges)= c("from","to")
149 
-
164 
+
150 165 
   # Re-map indices according to performance
151 166 
   new_title_list = rownames(scone_res$params) # Ordered by perfomance
152 167 
   new_title_list = c(new_title_list,
... ... 
@@ -166,90 +181,90 @@ sconeReport = function(x, methods,
166 181 
     edges[old_edges == old_ids[i]] = i
167 182 
   }
168 183 
   nodes = nodes[order(nodes$id),]
169 
-
184 
+
170 185 
   ## ----- Color Variable -----
171 186 
   cc <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"),brewer.pal(12,"Set3"),
172 187 
           brewer.pal(8, "Accent"))
173 
-
188 
+
174 189 
   ## ----- UI Definition -----
175 
-  ui <- fluidPage(
176 
-    titlePanel("SCONE Report Browser"),
177 
-
178 
-    sidebarLayout(
179 
-      sidebarPanel(
180 
-        selectInput("norm_code", label = "Normalization",
190 
+  ui <- shiny::fluidPage(
191 
+    shiny::titlePanel("SCONE Report Browser"),
192 
+
193 
+    shiny::sidebarLayout(
194 
+      shiny::sidebarPanel(
195 
+        shiny::selectInput("norm_code", label = "Normalization",
181 196 
                     choices = structure(as.list(rownames(scone_res$params)),
182 197 
                                         names = rownames(scone_res$params)),
183 198 
                     selected = rownames(scone_res$params)[1]),
184 
-        selectInput("color_code", label = "Stratify Plots by",
199 
+        shiny::selectInput("color_code", label = "Stratify Plots by",
185 200 
                     choices = list("Batch"="batch",
186 
-                                   "Biological Condition"="bio",
201 
+                                   "Biological Condition"="bio",
187 202 
                                    "PAM Cluster"="clust"),
188 203 
                     selected = "batch"),
189 
-        sliderInput("dim", label = "Reduced Dimension (PCA)",
204 
+        shiny::sliderInput("dim", label = "Reduced Dimension (PCA)",
190 205 
                     min=2, max=min(length(batch)-1,10),
191 206 
                     value = 3),
192 
-        sliderInput("k", label = "Number of Clusters (PAM)",
207 
+        shiny::sliderInput("k", label = "Number of Clusters (PAM)",
193 208 
                     min=2, max=10,
194 209 
                     value = 2),
195 
-        helpText(paste0("Please note that with many cells (>500),",
210 
+        shiny::helpText(paste0("Please note that with many cells (>500),",
196 211 
                         " some plots may take several seconds ",
197 212 
                         "(sometimes minutes) to appear.")),
198 
-        downloadLink('downloadData', 'Download')
213 
+        shiny::downloadLink('downloadData', 'Download')
199 214 
       ),
200 
-
201 
-      mainPanel(tabsetPanel(type = "tabs",
202 
-                            tabPanel("Overview",
203 
-                                     br(),
215 
+
216 
+      shiny::mainPanel(shiny::tabsetPanel(type = "tabs",
217 
+                            shiny::tabPanel("Overview",
218 
+                                     shiny::br(),
204 219 
                                      visNetworkOutput("norm_net",
205 220 
                                                       width = "650px"),
206 
-                                     br(),
221 
+                                     shiny::br(),
207 222 
                                      DT::dataTableOutput('tbl_score')
208 223 
                             ),
209 
-                            tabPanel("PCA",
210 
-                                     br(),
211 
-                                     p(paste0("This panel shows principal ",
224 
+                            shiny::tabPanel("PCA",
225 
+                                     shiny::br(),
226 
+                                     shiny::p(paste0("This panel shows principal ",
212 227 
                                               "component analysis ",
213 228 
                                               "(PCA) results",
214 229 
                                               " on different subsets ",
215 230 
                                               "of genes.")),
216 
-                                     br(),
217 
-                                     h6("Variance Explained (All Genes)"),
218 
-                                     p(paste0("Use this plot",
231 
+                                     shiny::br(),
232 
+                                     shiny::h6("Variance Explained (All Genes)"),
233 
+                                     shiny::p(paste0("Use this plot",
219 234 
                                               " to decide on the ",
220 235 
                                               "dimensionality of the reduced",
221 236 
                                               " spaced used for evaluation.")),
222 
-                                     plotOutput("plot_scree",width = "650px",
237 
+                                     shiny::plotOutput("plot_scree",width = "650px",
223 238 
                                                 height = "400px"),
224 
-                                     br(),
225 
-                                     h6("2D (All Genes)"),
226 
-                                     plotOutput("plot_base",
239 
+                                     shiny::br(),
240 
+                                     shiny::h6("2D (All Genes)"),
241 
+                                     shiny::plotOutput("plot_base",
227 242 
                                                 width = "650px",
228 243 
                                                 height = "450px"),
229 
-                                     br(),
230 
-                                     h6("3D Interactive (All Genes)"),
244 
+                                     shiny::br(),
245 
+                                     shiny::h6("3D Interactive (All Genes)"),
231 246 
                                      plotlyOutput("plot3d_base",
232 247 
                                                   width = "650px",
233 248 
                                                   height = "650px"),
234 
-                                     br(),
235 
-                                     selectInput("gene_set",
249 
+                                     shiny::br(),
250 
+                                     shiny::selectInput("gene_set",
236 251 
                                                  label = "Gene selection",
237 
-                                                 choices =
252 
+                                                 choices =
238 253 
                                                    list("Negative Controls"=
239 254 
                                                           "neg",
240 255 
                                                         "Positive Controls"=
241 256 
                                                           "pos"),
242 257 
                                                  selected = "neg"),
243 
-                                     h6("2D (Select Genes)"),
244 
-                                     p(paste0("Visualize cells by PCs",
258 
+                                     shiny::h6("2D (Select Genes)"),
259 
+                                     shiny::p(paste0("Visualize cells by PCs",
245 260 
                                               " of control gene sets.")),
246 
-                                     plotOutput("plot_select",
261 
+                                     shiny::plotOutput("plot_select",
247 262 
                                                 width = "650px",
248 263 
                                                 height = "450px")
249 264 
                             ),
250 
-                            tabPanel("QC",
251 
-                                     br(),
252 
-                                     p(paste0("This panel shows the absolute",
265 
+                            shiny::tabPanel("QC",
266 
+                                     shiny::br(),
267 
+                                     shiny::p(paste0("This panel shows the absolute",
253 268 
                                               " correlations between",
254 269 
                                               " Principal",
255 270 
                                               " Components (PCs) of",
... ... 
@@ -264,30 +279,30 @@ sconeReport = function(x, methods,
264 279 
                                               " could have a low value of ",
265 280 
                                               "PC1 and 'bad' cells a",
266 281 
                                               " high value).")),
267 
-                                     br(),
282 
+                                     shiny::br(),
268 283 
                                      plotlyOutput('qccorPlot',
269 284 
                                                   width = "650px",
270 285 
                                                   height = "450px"),
271 
-                                     h5("PCA of QC metrics"),
272 
-                                     h6("2D"),
273 
-                                     plotOutput("plot_qc",
286 
+                                     shiny::h5("PCA of QC metrics"),
287 
+                                     shiny::h6("2D"),
288 
+                                     shiny::plotOutput("plot_qc",
274 289 
                                                 width = "650px",
275 290 
                                                 height = "450px"),
276 
-                                     h6("3D Interactive"),
291 
+                                     shiny::h6("3D Interactive"),
277 292 
                                      plotlyOutput("plot3d_qc",
278 293 
                                                   width = "650px",
279 294 
                                                   height = "650px")
280 295 
                             ),
281 
-                            tabPanel("Silhouette",
282 
-                                     br(),
283 
-                                     p(paste0("Silhouette Width per",
296 
+                            shiny::tabPanel("Silhouette",
297 
+                                     shiny::br(),
298 
+                                     shiny::p(paste0("Silhouette Width per",
284 299 
                                               " Sample and Contingency",
285 300 
                                               " Tables")),
286 
-                                     br(),
287 
-                                     plotOutput("plotsil",
301 
+                                     shiny::br(),
302 
+                                     shiny::plotOutput("plotsil",
288 303 
                                                 width = "650px",
289 304 
                                                 height = "450px"),
290 
-                                     selectInput("cat1",
305 
+                                     shiny::selectInput("cat1",
291 306 
                                                  label =
292 307 
                                                    "Row Class",
293 308 
                                                  choices =
... ... 
@@ -297,7 +312,7 @@ sconeReport = function(x, methods,
297 312 
                                                           "bio", "PAM Cluster"=
298 313 
                                                           "clust"),
299 314 
                                                  selected = "batch"),
300 
-                                     selectInput("cat2",
315 
+                                     shiny::selectInput("cat2",
301 316 
                                                  label = "Column Class",
302 317 
                                                  choices =
303 318 
                                                    list("Batch"=
... ... 
@@ -307,33 +322,33 @@ sconeReport = function(x, methods,
307 322 
                                                         "PAM Cluster"=
308 323 
                                                           "clust"),
309 324 
                                                  selected = "bio"),
310 
-                                     tableOutput("cat_tab")
325 
+                                     shiny::tableOutput("cat_tab")
311 326 
                             ),
312 
-                            tabPanel("Control Genes",
313 
-                                     br(),
314 
-                                     p(paste0("Heatmap of control genes, ",
327 
+                            shiny::tabPanel("Control Genes",
328 
+                                     shiny::br(),
329 
+                                     shiny::p(paste0("Heatmap of control genes, ",
315 330 
                                               "colored by all",
316 331 
                                               " three categories.")),
317 
-                                     br(),
318 
-                                     p("Positive controls:"),
319 
-                                     plotOutput("hmposcon",
332 
+                                     shiny::br(),
333 
+                                     shiny::p("Positive controls:"),
334 
+                                     shiny::plotOutput("hmposcon",
320 335 
                                                 width = "650px",
321 336 
                                                 height = "450px"),
322 
-                                     p("Negative controls:"),
323 
-                                     plotOutput("hmnegcon",
337 
+                                     shiny::p("Negative controls:"),
338 
+                                     shiny::plotOutput("hmnegcon",
324 339 
                                                 width = "650px",
325 340 
                                                 height = "450px")
326 341 
                             ),
327 
-
328 
-                            tabPanel("Stratified PCA",
329 
-                                     br(),
330 
-                                     p(paste0("Violin plots showing",
342 
+
343 
+                            shiny::tabPanel("Stratified PCA",
344 
+                                     shiny::br(),
345 
+                                     shiny::p(paste0("Violin plots showing",
331 346 
                                               " conditional ",
332 347 
                                               "distributions of",
333 348 
                                               " selected Principal",
334 349 
                                               " Component.")),
335 
-                                     br(),
336 
-                                     sliderInput("pcsel",
350 
+                                     shiny::br(),
351 
+                                     shiny::sliderInput("pcsel",
337 352 
                                                  label =
338 353 
                                                    paste0("Selected",
339 354 
                                                           " Principal ",
... ... 
@@ -341,11 +356,11 @@ sconeReport = function(x, methods,
341 356 
                                                  min=1, max=
342 357 
                                                    min(length(batch)-1,10),
343 358 
                                                  value = 1),
344 
-                                     h6("All Genes:"),
345 
-                                     plotOutput("violin_base",
359 
+                                     shiny::h6("All Genes:"),
360 
+                                     shiny::plotOutput("violin_base",
346 361 
                                                 width = "650px",
347 362 
                                                 height = "450px"),
348 
-                                     selectInput("gene_set2",
363 
+                                     shiny::selectInput("gene_set2",
349 364 
                                                  label = "Gene selection",
350 365 
                                                  choices =
351 366 
                                                    list("Negative Controls"=
... ... 
@@ -353,74 +368,74 @@ sconeReport = function(x, methods,
353 368 
                                                         "Positive Controls"=
354 369 
                                                           "pos"),
355 370 
                                                  selected = "neg"),
356 
-                                     h6("Select Genes:"),
357 
-                                     plotOutput("violin_select",
371 
+                                     shiny::h6("Select Genes:"),
372 
+                                     shiny::plotOutput("violin_select",
358 373 
                                                 width = "650px",
359 374 
                                                 height = "450px")
360 375 
                             ),
361 
-
362 
-                            tabPanel("Relative Log-Expression",
363 
-                                     br(),
364 
-                                     p(paste0("Relative ",
376 
+
377 
+                            shiny::tabPanel("Relative Log-Expression",
378 
+                                     shiny::br(),
379 
+                                     shiny::p(paste0("Relative ",
365 380 
                                               "Log-Expression Plot ",
366 381 
                                               "for top 100 Most ",
367 382 
                                               "Variable Genes.")),
368 
-                                     plotOutput("rle",
383 
+                                     shiny::plotOutput("rle",
369 384 
                                                 width = "850px",
370 385 
                                                 height = "450px")
371 386 
                             )
372 387 
       ))))
373 
-
388 
+
374 389 
   server <- function(input, output, session) {
375 
-
390 
+
376 391 
     ## ------ Overview Tab ------
377 
-
392 
+
378 393 
     # Render Network
379 394 
     output$norm_net <- renderVisNetwork({
380 
-
395 
+
381 396 
       # Awk: Check if any non-loaded methods are included
382 397 
       if(any(!all_nodes  %in% leaves)){
383 
-        visNetwork(nodes, edges,width = "100%",main = "Tree of Methods") %>%
384 
-          visHierarchicalLayout(sortMethod = "directed" )  %>%
385 
-          visGroups(groupname = "NoData", shape = "dot",
398 
+        visNetwork(nodes, edges,width = "100%",main = "Tree of Methods") %>%
399 
+          visHierarchicalLayout(sortMethod = "directed" )  %>%
400 
+          visGroups(groupname = "NoData", shape = "dot",
386 401 
                     size = 10, color = list(background = "lightgrey",
387 402 
                                             border = "darkblue",
388 
-                                            highlight =
403 
+                                            highlight =
389 404 
                                               list(background =
390 405 
                                                      "black",
391 
-                                                   border = "red")),
392 
-                    shadow = list(enabled = TRUE)) %>%
393 
-          visGroups(groupname = "Loaded", shape = "dot",
406 
+                                                   border = "red")),
407 
+                    shadow = list(enabled = TRUE)) %>%
408 
+          visGroups(groupname = "Loaded", shape = "dot",
394 409 
                     size = 20, color = list(background = "lightblue",
395 
-                                            border = "darkblue",
396 
-                                            highlight =
410 
+                                            border = "darkblue",
411 
+                                            highlight =
397 412 
                                               list(background = "cyan",
398 413 
                                                    border = "red")),
399 414 
                     shadow = list(enabled = TRUE)) %>%
400 415 
           visEdges(shadow = TRUE, arrows = list(to = list(enabled = TRUE)),
401 416 
                    color = list(color = "darkblue", highlight = "red")) %>%
402 417 
           visOptions(nodesIdSelection =
403 
-                       list(enabled = TRUE,
418 
+                       list(enabled = TRUE,
404 419 
                             values = nodes$id[nodes$group == "Loaded"],
405 420 
                             selected =
406 421 
                               nodes$id[nodes$title ==
407 422 
                                          rownames(scone_res$params)[1]])) %>%
408 423 
           visLegend(width = 0.1, position = "right", main = "Status")
409 424 
       }else{
410 
-        visNetwork(nodes, edges,width = "100%",main = "Tree of Methods") %>%
411 
-          visHierarchicalLayout(sortMethod = "directed" )  %>%
425 
+        visNetwork(nodes, edges,width = "100%",main = "Tree of Methods") %>%
426 
+          visHierarchicalLayout(sortMethod = "directed" )  %>%
412 427 
           visEdges(shadow = TRUE, arrows = list(to = list(enabled = TRUE)),
413 428 
                    color = list(color = "darkblue", highlight = "red")) %>%
414 429 
           visOptions(nodesIdSelection =
415 
-                       list(enabled = TRUE,
430 
+                       list(enabled = TRUE,
416 431 
                             values = nodes$id[nodes$group == "Loaded"],
417 
-                            selected =
432 
+                            selected =
418 433 
                               nodes$id[nodes$title ==
419 434 
                                          rownames(scone_res$params)[1]]))
420 435 
       }
421 
-
436 
+
422 437 
     })
423 
-
438 
+
424 439 
     # Render Table
425 440 
     output$tbl_score <- DT::renderDataTable({
426 441 
       DT::datatable(scone_res$scores,
... ... 
@@ -432,21 +447,21 @@ sconeReport = function(x, methods,
432 447 
                       list(columnDefs =
433 448 
                              list(list(visible=FALSE,
434 449 
                                        targets=1:(ncol(scone_res$scores)-1))),
435 
-                           dom = 'Bfrtip',
450 
+                           dom = 'Bfrtip',
436 451 
                            buttons = list(
437 452 
                              list(extend = 'colvis',
438 453 
                                   columns = c(1:(ncol(scone_res$scores)-1))))
439 454 
                       )) %>%
440 455 
         DT::formatSignif(columns=c(1:(ncol(scone_res$scores))), digits=3)
441 456 
     })
442 
-
457 
+
443 458 
     # Update Menu Upon Network Selection of (Loaded) Normalization
444 
-    observeEvent(input$norm_net_selected,{
445 
-      if(is.character(input$norm_net_selected)){ # probably unnecessary
459 
+    shiny::observeEvent(input$norm_net_selected,{
460 
+      if(is.character(input$norm_net_selected)){ # probably unnecessary
446 461 
         if(!input$norm_net_selected == ""){ # probably unnecessary
447 462 
           if(as.integer(input$norm_net_selected) %in%
448 463 
              nodes$id[nodes$group == "Loaded"]){
449 
-            updateSelectInput(
464 
+            shiny::updateSelectInput(
450 465 
               session,
451 466 
               "norm_code",
452 467 
               selected = as.character(
... ... 
@@ -457,12 +472,12 @@ sconeReport = function(x, methods,
457 472 
         }
458 473 
       }
459 474 
     })
460 
-
475 
+
461 476 
     # Update Menu Upon Table Selection
462 
-    observeEvent(input$tbl_score_rows_selected,{
463 
-      if(length(input$tbl_score_rows_selected) > 0 ){ # probably unnecessary
464 
-        updateSelectInput(session,
465 
-                          "norm_code",
477 
+    shiny::observeEvent(input$tbl_score_rows_selected,{
478 
+      if(length(input$tbl_score_rows_selected) > 0 ){ # probably unnecessary
479 
+        shiny::updateSelectInput(session,
480 
+                          "norm_code",
466 481 
                           selected = as.character(
467 482 
                             nodes$title[nodes$id ==
468 483 
                                           as.integer(
... ... 
@@ -472,22 +487,22 @@ sconeReport = function(x, methods,
472 487 
         )
473 488 
       }
474 489 
     })
475 
-
490 
+
476 491 
     # Upon Menu Selection of Normalization
477 
-    observeEvent(input$norm_code,{
478 
-
479 
-      # Update Network
492 
+    shiny::observeEvent(input$norm_code,{
493 
+
494 
+      # Update Network
480 495 
       if(is.character(input$norm_net_selected)){
481 
-
496 
+
482 497 
         # If selection is empty, then no valid node is selected.
483 498 
         if(input$norm_net_selected == ""){
484 499 
           visNetworkProxy("norm_net") %>%
485 500 
             visSelectNodes(id =
486 501 
                              array(nodes$id[nodes$title == input$norm_code]))
487 502 
         }else{
488 
-
503 
+
489 504 
           # If selection is different from menu, then network must be updated.
490 
-          if(as.character(nodes$title[nodes$id ==
505 
+          if(as.character(nodes$title[nodes$id ==
491 506 
                                       as.integer(
492 507 
                                         input$norm_net_selected
493 508 
                                         )]) != input$norm_code){
... ... 
@@ -497,120 +512,120 @@ sconeReport = function(x, methods,
497 512 
           }
498 513 
         }
499 514 
       }
500 
-
515 
+
501 516 
       # Update Table
502 
-
517 
+
503 518 
       # If selection is empty, then no row is selected.
504 519 
       if(length(input$tbl_score_rows_selected) == 0){
505 
-        dataTableProxy("tbl_score") %>%
506 
-          selectRows(nodes$id[nodes$title == input$norm_code])
520 
+        DT::dataTableProxy("tbl_score") %>%
521 
+          DT::selectRows(nodes$id[nodes$title == input$norm_code])
507 522 
       }else{
508 
-
523 
+
509 524 
         # If selection is different from menu, then row must be updated.
510 525 
         if(input$tbl_score_rows_selected !=
511 526 
            nodes$id[nodes$title == input$norm_code]){
512 
-          dataTableProxy("tbl_score") %>%
513 
-            selectRows(nodes$id[nodes$title == input$norm_code])
527 
+          DT::dataTableProxy("tbl_score") %>%
528 
+            DT::selectRows(nodes$id[nodes$title == input$norm_code])
514 529 
         }
515 530 
       }
516 
-
531 
+
517 532 
     })
518 
-
533 
+
519 534 
     ## ------ PCA Tab ------
520 
-
521 
-    normle <- reactive({
535 
+
536 
+    normle <- shiny::reactive({
522 537 
       as.matrix(scone_res$normalized_data[[input$norm_code]])
523 538 
     })
524 
-
525 
-    strat_col <- reactive({
539 
+
540 
+    strat_col <- shiny::reactive({
526 541 
       switch(input$color_code,
527 542 
              bio = bio,
528 543 
              batch = batch,
529 544 
              clust = pam_obj()$clust)
530 545 
     })
531 
-
532 
-    pc_col <- reactive({
546 
+
547 
+    pc_col <- shiny::reactive({
533 548 
       cc[strat_col()]
534 549 
     })
535 
-
536 
-    pc_gene <- reactive({
550 
+
551 
+    pc_gene <- shiny::reactive({
537 552 
       switch(input$gene_set,
538 553 
              pos = intersect(poscon,rownames(normle())),
539 554 
              neg = intersect(negcon,rownames(normle())))
540 555 
     })
541 
-
542 
-    pc_obj_base <- reactive({
543 
-
556 
+
557 
+    pc_obj_base <- shiny::reactive({
558 
+
544 559 
       # If user has not provided evaluation projection
545 560 
       if(is.null(eval_proj)){
546 
-
561 
+
547 562 
         prcomp(t(normle()),
548 563 
                center = TRUE, scale = TRUE)
549 
-
564 
+
550 565 
       } else {
551 
-
566 
+
552 567 
         proj = eval_proj(normle(),
553 568 
                          eval_proj_args = eval_proj_args)
554 569 
         colnames(proj) = paste0("PC",1:ncol(proj))
555 
-
570 
+
556 571 
         pc_out = list(x = proj, sdev = apply(proj,2,sd))
557 
-
572 
+
558 573 
         pc_out
559 
-
574 
+
560 575 
       }
561 
-
576 
+
562 577 
     })
563 
-
564 
-    pc_obj_select <- reactive({
578 
+
579 
+    pc_obj_select <- shiny::reactive({
565 580 
       if(length(pc_gene()) > 0){
566 
-
581 
+
567 582 
         # If user has not provided evaluation projection
568 583 
         if(is.null(eval_proj)){
569 
-
584 
+
570 585 
           prcomp(t(normle()[pc_gene(),]),
571 586 
                  center = TRUE, scale = TRUE)
572 
-
587 
+
573 588 
         } else {
574 
-
589 
+
575 590 
           proj = eval_proj(normle()[pc_gene(),],
576 591 
                            eval_proj_args = eval_proj_args)
577 592 
           colnames(proj) = paste0("PC",1:ncol(proj))
578 
-
593 
+
579 594 
           pc_out = list(x = proj, sdev = apply(proj,2,sd))
580 
-
595 
+
581 596 
           pc_out
582 
-
597 
+
583 598 
         }
584 
-
599 
+
585 600 
       }else{
586 601 
         list()
587 602 
       }
588 603 
     })
589 
-
590 
-    pam_obj <- reactive({
604 
+
605 
+    pam_obj <- shiny::reactive({
591 606 
       cluster::pam(x = pc_obj_base()$x[,1:input$dim],k = input$k)
592 607 
     })
593 
-
594 
-    output$plot_scree <- renderPlot({
608 
+
609 
+    output$plot_scree <- shiny::renderPlot({
595 610 
       plot(pc_obj_base()$sdev[1:min(input$dim*2,
596 611 
                                     length(batch)-1)]^2/
597 
-             sum(pc_obj_base()$sdev^2),
612 
+             sum(pc_obj_base()$sdev^2),
598 613 
            typ = "l",
599 
-           ylab = "Proportion of Variance",
614 
+           ylab = "Proportion of Variance",
600 615 
            xlab = "PC Index",
601 616 
            log = "y")
602 617 
       abline(v = input$dim, col = "red",lty = 2)
603 618 
     })
604 
-
605 
-    output$plot_base <- renderPlot({
619 
+
620 
+    output$plot_base <- shiny::renderPlot({
606 621 
       par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
607 622 
       plot(pc_obj_base()$x[,1:2],col =  pc_col(), pch = 16)
608 
-      legend("topright", inset=c(-0.3,0),
609 
-             legend=levels(factor(strat_col())),
623 
+      legend("topright", inset=c(-0.3,0),
624 
+             legend=levels(factor(strat_col())),
610 625 
              fill = cc[sort(unique(factor(strat_col())))])
611 626 
     })
612 
-
613 
-    output$plot3d_base <- renderPlotly({
627 
+
628 
+    output$plot3d_base <- shiny::renderPlotly({
614 629 
       PC1 <- PC2 <- PC3 <- NULL
615 630 
       df <- setNames(data.frame(pc_obj_base()$x[,1:3]),
616 631 
                      c("PC1", "PC2", "PC3"))
... ... 
@@ -618,96 +633,96 @@ sconeReport = function(x, methods,
618 633 
               type = "scatter3d", mode = "markers",
619 634 
               marker = list(color=pc_col() ))
620 635 
     })
621 
-
622 
-    output$plot_select <- renderPlot({
623 
-
636 
+
637 
+    output$plot_select <- shiny::renderPlot({
638 
+
624 639 
       par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
625 
-
640 
+
626 641 
       if(length(pc_gene()) > 0){
627 
-
642 
+
628 643 
         plot(pc_obj_select()$x[,1:2],
629 644 
              col =  pc_col(), pch = 16)
630 
-        legend("topright", inset=c(-0.3,0),
645 
+        legend("topright", inset=c(-0.3,0),
631 646 
                legend=levels(factor(strat_col())),
632 647 
                fill = cc[sort(unique(factor(strat_col())))])
633 
-
648 
+
634 649 
       }else{
635 
-
650 
+
636 651 
         plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
637 652 
         text(0,labels = "Control gene set is empty.")
638 
-
653 
+
639 654 
       }
640 
-
655 
+
641 656 
     })
642 
-
643 
-    pam_obj <- reactive({
657 
+
658 
+    pam_obj <- shiny::reactive({
644 659 
       cluster::pam(x = pc_obj_base()$x[,1:input$dim],k = input$k)
645 660 
     })
646 
-
661 
+
647 662 
     ## ------ QC Tab ------
648 
-
649 
-    cor_qc <- reactive({
663 
+
664 
+    cor_qc <- shiny::reactive({
650 665 
       abs(cor(qc,pc_obj_base()$x))
651 666 
     })
652 
-
653 
-    output$qccorPlot <- renderPlotly({
667 
+
668 
+    output$qccorPlot <- shiny::renderPlotly({
654 669 
       metric <- value <- PC <- NULL
655 
-
656 
-      df = melt(cor_qc()[,1:input$dim])
670 
+
671 
+      df = reshape2::melt(cor_qc()[,1:input$dim])
657 672 
       colnames(df) = c("metric","PC","value")
658 673 
       df$metric = factor(df$metric,levels = colnames(qc))
659 674 
       df$PC = factor(df$PC,
660 675 
                      levels = paste0("PC",1:input$dim))
661 
-      p <- ggplot(data = df, aes(x = PC,
662 
-                                 fill = metric,
676 
+      p <- ggplot(data = df, aes(x = PC,
677 
+                                 fill = metric,
663 678 
                                  weight=value)) +
664 
-        geom_bar(position = "dodge") +
665 
-        ylim(0, 1) +
679 
+        geom_bar(position = "dodge") +
680 
+        ylim(0, 1) +
666 681 
         labs(x="PC of Expression",
667 
-             y="Absolute Correlation") +
682 
+             y="Absolute Correlation") +
668 683 
         theme(legend.title=element_blank())
669 684 
       ggplotly(p)
670 685 
     })
671 
-
672 
-    pc_obj_qc <- reactive({
686 
+
687 
+    pc_obj_qc <- shiny::reactive({
673 688 
       prcomp(as.matrix(qc),center = TRUE, scale = TRUE)
674 689 
     })
675 
-
676 
-    output$plot_qc <- renderPlot({
690 
+
691 
+    output$plot_qc <- shiny::renderPlot({
677 692 
       par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
678 693 
       plot(pc_obj_qc()$x[,1:2],col =  pc_col(), pch = 16)
679 694 
       legend("topright", inset=c(-0.3,0),
680 695 
              legend=levels(factor(strat_col())),
681 696 
              fill = cc[sort(unique(factor(strat_col())))])
682 697 
     })
683 
-
684 
-    output$plot3d_qc <- renderPlotly({
698 
+
699 
+    output$plot3d_qc <- shiny::renderPlotly({
685 700 
       if(ncol(pc_obj_qc()$x) >= 3){
686 701 
         PC1 <- PC2 <- PC3 <- NULL
687 702 
         df <- setNames(data.frame(pc_obj_qc()$x[,1:3]),
688 703 
                        c("PC1", "PC2", "PC3"))
689 
-        plot_ly(df, x = ~PC1, y = ~PC2, z = ~PC3,
690 
-                type = "scatter3d",
704 
+        plot_ly(df, x = ~PC1, y = ~PC2, z = ~PC3,
705 
+                type = "scatter3d",
691 706 
                 mode = "markers",
692 707 
                 marker = list(color=pc_col() ))
693 708 
       }else{
694 709 
         plot_ly(data.frame(), type = "scatter3d", mode = "markers")
695 710 
       }
696 711 
     })
697 
-
712 
+
698 713 
     ## ------ Silhouette Tab ------
699 
-
700 
-    sil_obj <- reactive({
714 
+
715 
+    sil_obj <- shiny::reactive({
701 716 
       cluster::silhouette(x = as.numeric(strat_col()),
702 717 
                           dist = dist(pc_obj_base()$x[,1:input$dim]))
703 718 
     })
704 
-
705 
-    output$plotsil <- renderPlot({
706 
-
719 
+
720 
+    output$plotsil <- shiny::renderPlot({
721 
+
707 722 
       par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
708 
-
723 
+
709 724 
       if(length(unique(strat_col())) > 1){
710 
-
725 
+
711 726 
         sil = sil_obj()
712 727 
         o1 = order(sil[,3])
713 728 
         o = o1[order(-sil[,1][o1])]
... ... 
@@ -717,205 +732,205 @@ sconeReport = function(x, methods,
717 732 
                 xlim = 1.5*range(c(sil[,3],-sil[,3])),
718 733 
                 col = pc_col()[o],
719 734 
                 border = pc_col()[o])
720 
-        legend("topright", inset=c(-0.3,0),
721 
-               legend=levels(factor(strat_col())),
735 
+        legend("topright", inset=c(-0.3,0),
736 
+               legend=levels(factor(strat_col())),
722 737 
                fill = cc[sort(unique(factor(strat_col())))])
723 
-
738 
+
724 739 
       }else{
725 
-
740 
+
726 741 
         plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
727 742 
         text(0,labels = "Stratify plots by a multi-level classification.")
728 
-
743 
+
729 744 
       }
730 
-
745 
+
731 746 
     })
732 
-
733 
-    cat1 <- reactive({
747 
+
748 
+    cat1 <- shiny::reactive({
734 749 
       switch(input$cat1,
735 750 
              bio = bio,
736 751 
              batch = batch,
737 752 
              clust = pam_obj()$clust)
738 753 
     })
739 
-
740 
-    cat2 <- reactive({
754 
+
755 
+    cat2 <- shiny::reactive({
741 756 
       switch(input$cat2,
742 757 
              bio = bio,
743 758 
              batch = batch,
744 759 
              clust = pam_obj()$clust)
745 760 
     })
746 
-
747 
-    output$cat_tab <- renderTable({
748 
-      table(cat1(), cat2())
761 
+
762 
+    output$cat_tab <- shiny::renderTable({
763 
+      table(cat1(), cat2())
749 764 
     })
750 
-
751 
-
765 
+
766 
+
752 767 
     ## ------ Control Genes Tab ------
753 
-
754 
-    silo <- reactive({
768 
+
769 
+    silo <- shiny::reactive({
755 770 
       sil = sil_obj()
756 771 
       o1 = order(sil[,3])
757 
-      o1[order(-sil[,1][o1])]
772 
+      o1[order(-sil[,1][o1])]
758 773 
     })
759 
-
760 
-    output$hmnegcon <- renderPlot({
761 
-
774 
+
775 
+    output$hmnegcon <- shiny::renderPlot({
776 
+
762 777 
       if(length(negcon) > 0){
763 
-
778 
+
764 779 
         if(length(unique(strat_col())) > 1){
765 
-
766 
-          aheatmap(normle()[negcon,], Colv = silo(),
780 
+
781 
+          NMF::aheatmap(normle()[negcon,], Colv = silo(),
767 782 
                    annCol = list(batch = batch, bio = bio,
768 783 
                                  pam =  as.factor(pam_obj()$clust)),
769 784 
                    annColors = list(batch = cc, bio = cc, pam =  cc))
770 
-
785 
+
771 786 
         }else{
772 
-
787 
+
773 788 
           plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
774 789 
           text(0,labels = "Stratify plots by a multi-level classification.")
775 
-
790 
+
776 791 
         }
777 
-
792 
+
778 793 
       }else{
779 
-
794 
+
780 795 
         plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
781 796 
         text(0,labels = "Control gene set is empty.")
782 
-
797 
+
783 798 
       }
784 
-
799 
+
785 800 
     })
786 
-
787 
-    output$hmposcon <- renderPlot({
788 
-
801 
+
802 
+    output$hmposcon <- shiny::renderPlot({
803 
+
789 804 
       if(length(poscon) > 0){
790 
-
805 
+
791 806 
         if(length(unique(strat_col())) > 1){
792 
-
793 
-          aheatmap(normle()[poscon,], Colv = silo(),
794 
-                   annCol = list(batch = batch,
795 
-                                 bio = bio,
807 
+
808 
+          NMF::aheatmap(normle()[poscon,], Colv = silo(),
809 
+                   annCol = list(batch = batch,
810 
+                                 bio = bio,
796 811 
                                  pam = as.factor(pam_obj()$clust)),
797 812 
                    annColors = list(batch = cc, bio = cc, pam =  cc))
798 
-
813 
+
799 814 
         }else{
800 
-
815 
+
801 816 
           plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
802 817 
           text(0,labels = "Stratify plots by a multi-level classification.")
803 
-
818 
+
804 819 
         }
805 
-
820 
+
806 821 
       }else{
807 
-
822 
+
808 823 
         plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
809 824 
         text(0,labels = "Control gene set is empty.")
810 
-
825 
+
811 826 
       }
812 
-
827 
+
813 828 
     })
814 
-
829 
+
815 830 
     ## ------ Stratified PCA Tab ------
816 
-
817 
-    output$violin_base <- renderPlot({
818 
-
831 
+
832 
+    output$violin_base <- shiny::renderPlot({
833 
+
819 834 
       Class = factor(strat_col())
820 
-      Val = pc_obj_base()$x[,input$pcsel]
821 
-
822 
-      ggplot(data.frame(Class,Val ),aes(x = Class,y = Val))   +
823 
-        geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
835 
+      Val = pc_obj_base()$x[,input$pcsel]
836 
+
837 
+      ggplot(data.frame(Class,Val ),aes(x = Class,y = Val))   +
838 
+        geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
824 839 
         labs(x = "Plot Stratification", y = "PC") +
825 840 
         coord_cartesian(ylim = max(abs(range(Val)))*c(-1.5,1.5))  +
826 841 
         scale_fill_manual(values=cc[sort(unique(factor(strat_col())))]) +
827 842 
         geom_point(colour = "black") + guides(fill=FALSE)
828 
-
843 
+
829 844 
     })
830 
-
831 
-    pc_gene2 <- reactive({
845 
+
846 
+    pc_gene2 <- shiny::reactive({
832 847 
       switch(input$gene_set2,
833 848 
              pos = intersect(poscon,rownames(normle())),
834 849 
              neg = intersect(negcon,rownames(normle())))
835 850 
     })
836 
-
837 
-    pc_obj_select2 <- reactive({
838 
-
851 
+
852 
+    pc_obj_select2 <- shiny::reactive({
853 
+
839 854 
       # If selected genes exist
840 855 
       if(length(pc_gene2()) > 0){
841 
-
856 
+
842 857 
         # If user has not provided evaluation projection
843 858 
         if(is.null(eval_proj)){
844 
-
859 
+
845 860 
           prcomp(t(normle()[pc_gene2(),]),
846 861 
                  center = TRUE, scale = TRUE)
847 
-
862 
+
848 863 
         } else {
849 
-
864 
+
850 865 
           proj = eval_proj(normle()[pc_gene2(),],
851 866 
                            eval_proj_args = eval_proj_args)
852 867 
           colnames(proj) = paste0("PC",1:ncol(proj))
853 
-
868 
+
854 869 
           pc_out = list(x = proj, sdev = apply(proj,2,sd))
855 
-
870 
+
856 871 
           pc_out
857 
-
872 
+
858 873 
         }
859 
-
874 
+
860 875 
       }else{
861 876 
         list()
862 877 
       }
863 
-
878 
+
864 879 
     })
865 
-
866 
-    output$violin_select <- renderPlot({
867 
-
880 
+
881 
+    output$violin_select <- shiny::renderPlot({
882 
+
868 883 
       if(length(pc_gene2()) > 0){
869 
-
884 
+
870 885 
         Class = factor(strat_col())
871 
-        Val = pc_obj_select2()$x[,input$pcsel]
872 
-
873 
-        ggplot(data.frame(Class,Val ),aes(x = Class,y = Val))   +
874 
-          geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
886 
+        Val = pc_obj_select2()$x[,input$pcsel]
887 
+
888 
+        ggplot(data.frame(Class,Val ),aes(x = Class,y = Val))   +
889 
+          geom_violin(scale = "width", trim = TRUE, aes(fill = Class))+
875 890 
           labs(x = "Plot Stratification", y = "PC") +
876 891 
           coord_cartesian(ylim = max(abs(range(Val)))*c(-1.5,1.5))  +
877 892 
           scale_fill_manual(values=cc[sort(unique(factor(strat_col())))]) +
878 893 
           geom_point(colour = "black") + guides(fill=FALSE)
879 
-
894 
+
880 895 
       }else{
881 
-
896 
+
882 897 
         plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
883 898 
         text(0,labels = "Control gene set is empty.")
884 
-
899 
+
885 900 
       }
886 901 
     })
887 
-
902 
+
888 903 
     ## ------ Relative Log-Expression ------
889 
-
890 
-    output$rle <- renderPlot({
891 
-
904 
+
905 
+    output$rle <- shiny::renderPlot({
906 
+
892 907 
       par(mar=c(5.1, 4.1, 4.1, 10.1), xpd=TRUE)
893 
-
908 
+
894 909 
       if(length(unique(strat_col())) > 1){
895 
-
910 
+
896 911 
         vars = apply(exp(normle()),1,var)
897 912 
         is_var = rank(-vars) <= 100
898 913 
         median <- apply(normle()[is_var,], 1, median)
899 914 
         rle <- apply(normle()[is_var,], 2, function(x) x - median)
900 
-        boxplot(rle[,rev(silo())],col = pc_col()[rev(silo())],
915 
+        boxplot(rle[,rev(silo())],col = pc_col()[rev(silo())],
901 916 
                 outline = FALSE, names = rep("",ncol(rle)))
902 917 
         abline(h=0, lty=2)
903 918 
         legend("topright", inset=c(-0.2,0),
904 
-               legend=levels(factor(strat_col())),
919 
+               legend=levels(factor(strat_col())),
905 920 
                fill = cc[sort(unique(factor(strat_col())))])
906 
-
921 
+
907 922 
       }else{
908 
-
923 
+
909 924 
         plot(0, type = 'n',xlab = "",ylab = "",xaxt = 'n',yaxt = 'n')
910 925 
         text(0,labels = "Stratify plots by a multi-level classification.")
911 
-
926 
+
912 927 
       }
913 
-
928 
+
914 929 
     })
915 
-
930 
+
916 931 
     ## ----- Download Button -----
917 
-
918 
-    output$downloadData <- downloadHandler(
932 
+
933 
+    output$downloadData <- shiny::downloadHandler(
919 934 
       filename = function() {
920 935 
         paste('scone_out-', Sys.Date(), '.csv', sep='')
921 936 
       },
... ... 
@@ -929,10 +944,10 @@ sconeReport = function(x, methods,
929 944 
         write.csv(datt, con)
930 945 
       }
931 946 
     )
932 
-
947 
+
933 948 
   }
934 
-
949 
+
935 950 
   # Shiny App
936 
-  shinyApp(ui = ui, server = server)
937 
-
951 
+  shiny::shinyApp(ui = ui, server = server)
952 
+
938 953 
 }
man/sconeReport.Rd
History View file @ d415a9d
... ... 
@@ -21,16 +21,16 @@ Default NULL.}
21 21 
 \item{batch}{factor. A known batch variable (variation to be removed).
22 22 
 Default NULL.}
23 23
24 
-\item{poscon}{character. Genes to be used as positive controls for
25 
-evaluation. These genes should be expected to change according to the
24 
+\item{poscon}{character. Genes to be used as positive controls for
25 
+evaluation. These genes should be expected to change according to the
26 26 
 biological phenomenon of interest. Default empty character.}
27 27
28 
-\item{negcon}{character. Genes to be used as negative controls for
29 
-evaluation. These genes should be expected not to change according to the
28 
+\item{negcon}{character. Genes to be used as negative controls for
29 
+evaluation. These genes should be expected not to change according to the
30 30 
 biological phenomenon of interest. Default empty character.}
31 31
32 
-\item{eval_proj}{function. Projection function for evaluation  (see
33 
-\code{\link{score_matrix}} for details). If NULL, PCA is used for
32 
+\item{eval_proj}{function. Projection function for evaluation  (see
33 
+\code{\link{score_matrix}} for details). If NULL, PCA is used for
34 34 
 projection.}
35 35
36 36 
 \item{eval_proj_args}{list. List of args passed to projection function as
... ... 
@@ -40,7 +40,7 @@ eval_proj_args.}
40 40 
 An object that represents the SCONE report app.
41 41 
 }
42 42 
 \description{
43 
-This function opens a shiny application session for visualizing performance
43 
+This function opens a shiny application session for visualizing performance
44 44 
 of a variety of normalization schemes.
45 45 
 }
46 46 
 \examples{
... ... 
@@ -49,7 +49,7 @@ mat <- matrix(rpois(1000, lambda = 5), ncol=10)
49 49 
 colnames(mat) <- paste("X", 1:ncol(mat), sep="")
50 50 
 obj <- SconeExperiment(mat)
51 51 
 res <- scone(obj, scaling=list(none=identity, uq=UQ_FN, deseq=DESEQ_FN),
52 
-           evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,
52 
+           evaluate=TRUE, k_ruv=0, k_qc=0, eval_kclust=2,
53 53 
            bpparam = BiocParallel::SerialParam())
54 54 
 qc = as.matrix(cbind(colSums(mat),colSums(mat > 0)))
55 55 
 rownames(qc) = colnames(mat)