orca fails when trying to save a heatmap

Hi,

I'm creating a heatmap image.
If I try to combine it with a column dendrogram, although it is viewable on the RStudio viewer, using orca to export it as pdf fails .

Here's an example where I build a sparse heatmap and add to it a column dendrogram:

library(ggplot2)
library(dplyr)
library(plotly)

#build the heatmap data.frame
row.text <- strsplit("ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP,ALLOGRAFT_REJECTION,APICAL_SURFACE,APOPTOSIS,EPITHELIAL_MESENCHYMAL_TRANSITION,ESTROGEN_RESPONSE_EARLY,HYPOXIA,IL2_STAT5_SIGNALING,IL6_JAK_STAT3_SIGNALING,INFLAMMATORY_RESPONSE,INTERFERON_ALPHA_RESPONSE,INTERFERON_GAMMA_RESPONSE,KRAS_SIGNALING_DN,KRAS_SIGNALING_UP,MYC_TARGETS_V1,P53_PATHWAY,PI3K_AKT_MTOR_SIGNALING,PROTEIN_SECRETION,TNFA_SIGNALING_VIA_NFKB,UV_RESPONSE_UP",split=",")[[1]]
col.text <- strsplit("APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,APC.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,B-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,T-cell.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Neutrophil.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm,Splenic-monocyte.nnn.vs.mmmmm",split=",")[[1]]
vals <- as.numeric(strsplit("1.82390874094432,NA,NA,1.72124639904717,NA,1.69897000433602,1.34678748622466,NA,2.74472749489669,3.76955107862173,3.76955107862173,NA,1.72124639904717,1.3767507096021,1.3767507096021,NA,NA,3.76955107862173,1.72124639904717,NA,NA,NA,NA,NA,NA,NA,NA,NA,3.60205999132796,3.60205999132796,NA,NA,NA,NA,NA,NA,NA,NA,2.20760831050175,NA,2.20760831050175,NA,NA,NA,NA,NA,NA,3.30102999566398,1.69897000433602,1.48148606012211,NA,NA,NA,NA,NA,NA,NA,2.72124639904717,NA,3.60205999132796,2.36653154442041,1.55284196865778,3.13667713987954,2.46852108295775,2.74472749489669,3.13667713987954,2.72124639904717,2.46852108295775,2.29242982390206,NA,NA,NA,2.72124639904717,1.3767507096021,3.60205999132796,NA,NA,1.30980391997149,NA,NA,NA,1.30980391997149,NA,NA,NA,1.79588001734408,NA,NA,NA,NA,NA,NA,NA,NA,1.30980391997149",split=",")[[1]])
plot.df <- data.frame(row = row.text, column = col.text, value = vals)

#build the column dendrogram, via clustering
plot.mat <- reshape2::acast(plot.df,column ~ row)
col.hc <- cluster::agnes(plot.mat,diss=FALSE,method="complete")
sorted.col.hc <- dendsort::dendsort(as.hclust(col.hc))
col.dend <- as.dendrogram(sorted.col.hc)
col.dendex <- dendextend::as.ggdend(col.dend)
leaf.heights <- dplyr::filter(col.dendex$nodes,!is.na(leaf))$height
leaf.xs <- dplyr::filter(col.dendex$nodes,!is.na(leaf))$x
leaf.seqments.idx <- which(col.dendex$segments$yend %in% leaf.heights & col.dendex$segments$x %in% leaf.xs)
col.dendex$segments$yend[leaf.seqments.idx] <- max(col.dendex$segments$yend[leaf.seqments.idx])
col.dendex$segments$col[leaf.seqments.idx] <- "black"
col.dendex$labels$label <- ""
col.dendex$labels$y <- max(col.dendex$segments$yend[leaf.seqments.idx])
col.dendex$labels$x <- col.dendex$segments$x[leaf.seqments.idx]
col.dendex$labels$col <- "black"
col.dendex$segments$lwd <- 0.5
col.ggdend <- ggplot(col.dendex,labels=F)+guides(fill=F)+theme_minimal()+
  theme(axis.title=element_blank(),axis.text=element_blank(),axis.ticks=element_blank(),panel.grid=element_blank(),legend.position="none",legend.text=element_blank(),legend.background=element_blank(),legend.key=element_blank())

legend.title <- "-log10(Q-value)"
color.vec <- c("black","yellow")

#build the heatmap
p <- plot_ly(z=c(plot.df$value),x=plot.df$column,y=plot.df$row,colors=grDevices::colorRamp(color.vec),type="heatmap",colorbar=list(title=legend.title,len=0.4)) %>%
  layout(yaxis=list(title=NULL),xaxis=list(title=NULL))

#add the dendrogram
p <- plotly::subplot(col.ggdend,plotly::plotly_empty(),p %>% plotly::layout(showlegend = F),nrows=2,margin=c(0,0,0,0),heights=c(0.2,0.8),widths=c(0.8,0.2))

As mentioned above, I'm able to view the image of the heatmap + dendrogram on the RStudio viewer. However, when I try to save it to a pdf using:

plotly::orca(p, file="p.pdf")
I get the error:

done with code 1 in 41.38 sec - failed or incomplete task(s)
Error in processx::run("orca", args, echo = TRUE, spinner = TRUE, ...) : 
  System command error

The failure of exporting a heatmap to a pdf using orca is not specific to the example above though.

In the example below I'm creating a big heatmap (1862 x 2511) with long row and column labels and it also fails:

The heatmap is facetted by columns, so first I create a data.frame fr guiding the facets:

set.seed(1)
facet.df <- data.frame(facet = 1:20,
                       n.cols = c(38,32,78,59,303,417,77,119,18,14,262,351,24,11,13,13,175,182,198,127),
                       x.tick.vals = c(296,248.5,616.5,463,2404,3316.5,608,941.5,135.5,103.5,2081.5,2791,191,86,100.5,100.5,1388.5,1447.5,1573,1003.5),stringsAsFactors = F)

row.labels <- paste0("RRRRR",1:1862)
col.labels <- paste0(paste(sample(c("A","C","G","T"),16,replace=T),collapse=""),"-1_aaa.",1:2511)

Now I create a list of heatmap plots, one per each facet:

plot.list <- lapply(1:nrow(facet.df),function(f){
  if(f > 1){
    x.vals <- col.labels[(sum(dplyr::filter(facet.df,facet <= f-1)$n.cols)+1):sum(dplyr::filter(facet.df,facet <= f)$n.cols)]
  } else{
    x.vals <- col.labels[1:facet.df$n.cols[f]]
  }
  facet.heatmap.df <- data.frame(value = rnorm(1862*facet.df$n.cols[f]), x = unlist(lapply(x.vals,function(x) rep(x,length(row.labels)))), y = rep(row.labels,facet.df$n.cols[f]),stringsAsFactors = F)
  plot_ly(z = c(facet.heatmap.df$value), x = facet.heatmap.df$x, y = facet.heatmap.df$y, colors = grDevices::colorRamp(c("cyan","black","yellow")), type = "heatmap",colorbar = list(title="expr",len=0.4)) %>%
    layout(yaxis=list(title=NULL),xaxis=list(tickangle=90,tickvals=facet.df$x.tick.vals[f]))
})

Now I combine the facet plots to a single plot:

heatmap.plot <- plotly::subplot(plot.list,shareX=T,shareY=T,nrows=1,margin=0.001,widths=rep(1/nrow(facet.df),nrow(facet.df))) %>% layout(showlegend=F)
Finally, trying to export the plot using orca:
`
plotly::orca(heatmap.plot %>% plotly::style(hoverinfo = 'none'),file="heatmap.plot.pdf")

`

Fails with:

done with code 1 in 204.44 ms - failed or incomplete task(s)
Error in processx::run("orca", args, echo = TRUE, spinner = TRUE, ...) : 
  System command error

Thanks a lot.

Bắt đầu tại plotly::orca với ví dụ heatmap-and-dendrogram nhỏ được cung cấp, sau đó so sánh với bản tái hiện heatmap lớn được phân tách theo facet. Hoàn thành khi cả hai ví dụ đều xuất thành công sang PDF mà không gặp processx system-command error.