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.

Commencez par plotly::orca avec le petit exemple heatmap-and-dendrogram fourni, puis comparez-le avec la reproduction de la grande heatmap facettée. C’est terminé lorsque les deux exemples s’exportent correctement au format PDF sans le processx system-command error.