Question: Error when launching shiny app in cytofkit
0
gravatar for crysis405
3.3 years ago by
crysis40510
crysis40510 wrote:

Error when launching shiny app in cytofkit:

launchShinyAPP_GUI("F:/Sync/cytometry/resultsQC/cytofkit.RData")

Error in FUN(X[[i]], ...) :
  cannot open file '~/R/win-library/3.3/reshape2/data/Rdata.rdb': No such file or directory

 

The file does exist:

> list.files('~/R/win-library/3.3/reshape2/data/')
[1] "Rdata.rdb" "Rdata.rds" "Rdata.rdx"

 

Session info:

R version 3.3.0 (2016-05-03)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United Kingdom.1252  LC_CTYPE=English_United Kingdom.1252    LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                            LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] reshape2_1.4.1 gplots_3.0.1   shiny_0.13.2   cytofkit_1.4.3 plyr_1.8.4     ggplot2_2.1.0 

loaded via a namespace (and not attached):
 [1] gtools_3.5.0        splines_3.3.0       lattice_0.20-33     tcltk_3.3.0         pcaPP_1.9-60        colorspace_1.2-6    htmltools_0.3.5    
 [8] stats4_3.3.0        mgcv_1.8-12         flowCore_1.38.2     e1071_1.6-7         BiocGenerics_0.18.0 matrixStats_0.50.2  foreach_1.4.3      
[15] robustbase_0.92-6   stringr_1.0.0       munsell_0.4.3       pdist_1.2           gtable_0.2.0        caTools_1.17.1      mvtnorm_1.0-5      
[22] codetools_0.2-14    VGAM_1.0-2          Biobase_2.32.0      permute_0.9-0       doParallel_1.0.10   httpuv_1.3.3        parallel_3.3.0     
[29] class_7.3-14        DEoptimR_1.0-4      Rcpp_0.12.5         KernSmooth_2.23-15  xtable_1.8-2        corpcor_1.6.8       scales_0.4.0       
[36] gdata_2.17.0        vegan_2.3-5         graph_1.50.0        mime_0.4            RANN_2.5            digest_0.6.9        stringi_1.1.1      
[43] Rtsne_0.10          grid_3.3.0          tools_3.3.0         bitops_1.0-6        magrittr_1.5        cluster_2.0.4       rrcov_1.3-11       
[50] Matrix_1.2-6        MASS_7.3-45         reshape_0.8.5       iterators_1.0.8     R6_2.1.2            igraph_1.0.1        nlme_3.1-128   

Traceback:

> traceback()
14: FUN(X[[i]], ...)
13: vapply(same, exists, NA, where = where, mode = "function", inherits = FALSE)
12: same.isFn(i)
11: checkConflicts(package, pkgname, pkgpath, nogenerics, ns)
10: library(reshape)
9: ..stacktraceon..({
       library(ggplot2)
       library(gplots)
       library(reshape2)
       library(reshape)
       library(plyr)
       library(VGAM)
       visuaPlot <- function(obj, xlab, ylab, zlab, pointSize = 1, 
           addLabel = TRUE, labelSize = 1, selectSamples, removeOutlier = TRUE) {
           data <- cbind(obj$allExpressionData, do.call(cbind, obj$dimReducedRes))
           data <- as.data.frame(data)
           clusterMethods <- names(obj$clusterRes)
           for (cname in clusterMethods) {
               data[[cname]] <- as.factor(obj$clusterRes[[cname]])
           }
           row.names(data) <- row.names(obj$expressionData)
           samples <- sub("_[0-9]*$", "", row.names(obj$expressionData))
           data <- data[samples %in% selectSamples, ]
           nsamples <- samples[samples %in% selectSamples]
           data$sample <- nsamples
           sample_num <- length(unique(nsamples))
           if (sample_num >= 8) {
               shape_value <- LETTERS[1:sample_num]
           }
           else {
               shape_value <- c(1:sample_num) + 15
           }
           if (zlab %in% clusterMethods) {
               cluster_num <- length(unique(data[[zlab]]))
               col_legend_row <- ceiling(cluster_num/15)
               size_legend_row <- ceiling(sample_num/4)
               shapeLab <- "sample"
               gp <- ggplot(data, aes_string(x = xlab, y = ylab, 
                   colour = zlab, shape = shapeLab)) + geom_point(size = pointSize) + 
                   scale_shape_manual(values = shape_value) + scale_colour_manual(values = rainbow(cluster_num)) + 
                   xlab(xlab) + ylab(ylab) + guides(colour = guide_legend(nrow = col_legend_row, 
                   override.aes = list(size = 4)), shape = guide_legend(nrow = size_legend_row, 
                   override.aes = list(size = 4))) + theme_bw() + 
                   theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())
               if (addLabel) {
                   edata <- data[, c(xlab, ylab, zlab)]
                   colnames(edata) <- c("x", "y", "z")
                   center <- aggregate(cbind(x, y) ~ z, data = edata, 
                     median)
                   gp <- gp + annotate("text", label = center[, 
                     1], x = center[, 2], y = center[, 3], size = labelSize, 
                     colour = "black")
               }
               gp <- gp + theme(legend.position = "bottom", axis.text = element_text(size = 14), 
                   axis.title = element_text(size = 18, face = "bold"))
           }
           else {
               title <- zlab
               data <- data[, c(xlab, ylab, zlab)]
               if (removeOutlier) 
                   data[, zlab] <- remove_outliers(data[, zlab])
               zlab <- "Expression"
               colnames(data) <- c(xlab, ylab, zlab)
               gp <- ggplot(data, aes_string(x = xlab, y = ylab, 
                   colour = zlab)) + geom_point(size = pointSize) + 
                   theme_bw() + scale_colour_gradient2(low = "blue", 
                   mid = "white", high = "red", midpoint = median(data[[zlab]])) + 
                   theme(legend.position = "right") + xlab(xlab) + 
                   ylab(ylab) + ggtitle(title) + theme(panel.grid.major = element_blank(), 
                   panel.grid.minor = element_blank()) + theme(axis.text = element_text(size = 14), 
                   axis.title = element_text(size = 18, face = "bold"))
           }
           return(gp)
       }
       heatMap <- function(data, clusterMethod = "DensVM", type = "mean", 
           selectSamples, cex_row_label = 1, cex_col_label = 1, 
           scaleMethod = "none") {
           exprs <- data$expressionData
           samples <- sub("_[0-9]*$", "", row.names(exprs))
           exprs <- exprs[samples %in% selectSamples, ]
           ifMultiFCS <- length(selectSamples) > 1
           dataj <- data$clusterRes[[clusterMethod]][samples %in% 
               selectSamples]
           exprs_cluster <- data.frame(exprs, cluster = dataj)
           if (type == "mean") {
               cluster_stat <- aggregate(. ~ cluster, data = exprs_cluster, 
                   mean)
               rownames(cluster_stat) <- paste("cluster_", cluster_stat$cluster, 
                   sep = "")
               cluster_stat <- cluster_stat[, -which(colnames(cluster_stat) == 
                   "cluster")]
           }
           else if (type == "median") {
               cluster_stat <- aggregate(. ~ cluster, data = exprs_cluster, 
                   median)
               rownames(cluster_stat) <- paste("cluster_", cluster_stat$cluster, 
                   sep = "")
               cluster_stat <- cluster_stat[, -which(colnames(cluster_stat) == 
                   "cluster")]
           }
           else if (type == "percentage" && ifMultiFCS) {
               sampleName <- sub("_[0-9]*$", "", row.names(exprs))
               clusterCounts <- as.data.frame(table(sampleName, 
                   dataj))
               colnames(clusterCounts) <- c("sample", "cluster", 
                   "cellCount")
               sampleCellCount <- as.data.frame(table(sampleName))
               colnames(sampleCellCount) <- c("sample", "totalCellCount")
               clust_cellCount <- merge(clusterCounts, sampleCellCount, 
                   by = "sample")
               clust_cellCount$percentage <- round(clust_cellCount$cellCount/clust_cellCount$totalCellCount * 
                   100, 2)
               cluster_stat <- reshape::cast(clust_cellCount, sample ~ 
                   cluster, value = "percentage")
               percColNames <- cluster_stat$sample
               cluster_stat <- cluster_stat[, -which(colnames(cluster_stat) == 
                   "sample")]
               percRowNames <- paste("cluster_", colnames(cluster_stat), 
                   sep = "")
               cluster_stat <- t(as.matrix(cluster_stat))
               row.names(cluster_stat) <- percRowNames
               colnames(cluster_stat) <- percColNames
           }
           else {
               return(NULL)
           }
           cluster_stat <- as.matrix(cluster_stat)
           heatmap.2(cluster_stat, col = bluered, trace = "none", 
               symbreaks = FALSE, scale = scaleMethod, margins = c(8, 
                   8), cexRow = cex_row_label, cexCol = cex_col_label, 
               srtCol = 30, symkey = FALSE, keysize = 1, key.par = list(mgp = c(2, 
                   1, 0), mar = c(4, 3, 4, 0)), main = paste(clusterMethod, 
                   type, "heatmap", sep = " "))
       }
       progressionPlot <- function(data, orderCol = "isomap_1", 
           clusterCol = "cluster", trend_formula = "expression ~ sm.ns(Pseudotime, df=3)") {
           progressionData <- data$progressionRes
           if (!is.null(progressionData)) {
               data <- do.call(cbind, progressionData)
               markers <- colnames(progressionData[[1]])
               colnames(data) <- c(markers, "cluster", colnames(progressionData[[3]]))
               if (!is.data.frame(data)) 
                   data <- data.frame(data, check.names = FALSE)
               if (!all(markers %in% colnames(data))) 
                   stop("Unmatching markers found!")
               if (!(length(orderCol) == 1 && orderCol %in% colnames(data))) 
                   stop("Can not find orderCol in data")
               if (!(length(clusterCol) == 1 && clusterCol %in% 
                   colnames(data))) 
                   stop("Can not find clusterCol in data")
               orderValue <- data[[orderCol]]
               data <- data[order(orderValue), c(markers, clusterCol)]
               data$Pseudotime <- sort(orderValue)
               mdata <- melt(data, id.vars = c("Pseudotime", clusterCol))
               colnames(mdata) <- c("Pseudotime", clusterCol, "markers", 
                   "expression")
               mdata$markers <- factor(mdata$markers)
               mdata[[clusterCol]] <- factor(mdata[[clusterCol]])
               min_expr <- min(mdata$expression)
               vgamPredict <- ddply(mdata, .(markers), function(x) {
                   fit_res <- tryCatch({
                     vg <- suppressWarnings(vgam(formula = as.formula(trend_formula), 
                       family = VGAM::tobit(Lower = min_expr, lmu = "identitylink"), 
                       data = x, maxit = 30, checkwz = FALSE))
                     res <- VGAM::predict(vg, type = "response")
                     res[res < min_expr] <- min_expr
                     res
                   }, error = function(e) {
                     print("Error!")
                     print(e)
                     res <- rep(NA, nrow(x))
                     res
                   })
                   expectation = fit_res
                   data.frame(Pseudotime = x$Pseudotime, expectation = expectation)
               })
               color_by <- clusterCol
               plot_cols <- round(sqrt(length(markers)))
               cell_size <- 1
               x_lab <- orderCol
               y_lab <- "Expression"
               legend_title <- clusterCol
               monocle_theme_opts <- function() {
                   theme(strip.background = element_rect(colour = "white", 
                     fill = "white")) + theme(panel.border = element_blank(), 
                     axis.line = element_line()) + theme(panel.grid.minor.x = element_blank(), 
                     panel.grid.minor.y = element_blank()) + theme(panel.grid.major.x = element_blank(), 
                     panel.grid.major.y = element_blank()) + theme(panel.background = element_rect(fill = "white")) + 
                     theme(legend.position = "right") + theme(axis.title = element_text(size = 15))
               }
               q <- ggplot(aes(Pseudotime, expression), data = mdata)
               q <- q + geom_point(aes_string(color = color_by), 
                   size = I(cell_size))
               q <- q + geom_line(aes(Pseudotime, expectation), 
                   data = vgamPredict)
               q <- q + facet_wrap(~markers, ncol = plot_cols, scales = "free_y")
               q <- q + ylab(y_lab) + xlab(x_lab) + theme_bw()
               q <- q + guides(colour = guide_legend(title = legend_title, 
                   override.aes = list(size = cell_size * 3)))
               q <- q + monocle_theme_opts()
               return(q)
           }
           else {
               return(NULL)
           }
       }
       remove_outliers <- function(x, na.rm = TRUE, ...) {
           qnt <- quantile(x, probs = c(0.25, 0.75), na.rm = na.rm, 
               ...)
           H <- 1.5 * IQR(x, na.rm = na.rm)
           y <- x
           y[x < (qnt[1] - H)] <- qnt[1] - H
           y[x > (qnt[2] + H)] <- qnt[2] + H
           y
       }
   })
8: eval(expr, envir, enclos)
7: eval(exprs, envir)
6: sourceUTF8(file.path.ci(appDir, "global.R"))
5: appParts$onStart()
4: shiny::runApp(system.file("shiny", package = "cytofkit"))
3: cytofkitShinyAPP()
2: launchShinyAPP_GUI(okMessage)
1: cytofkit_GUI()
cytofkit • 834 views
ADD COMMENTlink modified 3.3 years ago by chen_hao30 • written 3.3 years ago by crysis40510
Answer: Error when launching shiny app in cytofkit
0
gravatar for chen_hao
3.3 years ago by
chen_hao30
Singapore
chen_hao30 wrote:
Hi, Thanks for reporting the error. Actually you can open the shiny APP by calling cytofkitShinyAPP() Then on shiny app, loading in your .RData then submit. Best Regards, Chen Hao On 10 Jun 2016, at 7:39 PM, crysis405 [bioc] <noreply@bioconductor.org<mailto:noreply@bioconductor.org>> wrote: cytofkitShinyAPP
ADD COMMENTlink written 3.3 years ago by chen_hao30

I get the same error:

> cytofkitShinyAPP()
Loading required package: shiny

Attaching package: ‘gplots’

The following object is masked from ‘package:stats’:

    lowess

Error in FUN(X[[i]], ...) :
  cannot open file '~/R/win-library/3.3/reshape/data/Rdata.rdb': No such file or directory
ADD REPLYlink modified 3.3 years ago • written 3.3 years ago by crysis40510
Could you update your update cytofkit to version 1.4.5 and, update Rstudio as well if you think it was a problem. Or you can also use the online version https://chenhao.shinyapps.io/cytofkitShinyAPP/ Best Regards, Chen Hao On 11 Jun 2016, at 12:04 AM, crysis405 [bioc] <noreply@bioconductor.org<mailto:noreply@bioconductor.org>> wrote: Activity on a post you are following on support.bioconductor.org<https: support.bioconductor.org=""/> User crysis405<https: support.bioconductor.org="" u="" 10875=""/> wrote Comment: Error when launching shiny app in cytofkit<https: support.bioconductor.org="" p="" 83648="" #83673="">: I get the same error: > cytofkitShinyAPP() Loading required package: shiny Attaching package: ‘gplots’ The following object is masked from ‘package:stats’: lowess Error in FUN(X[[i]], ...) : cannot open file '~/R/win-library/3.3/reshape/data/Rdata.rdb': No such file or directory ________________________________ Post tags: cytofkit You may reply via email or visit C: Error when launching shiny app in cytofkit
ADD REPLYlink written 3.3 years ago by chen_hao30

The error seems to only be produced when running the command from within RStudio

ADD REPLYlink written 3.3 years ago by crysis40510
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