text: user39: I am curious how people make genome track plots such as what can be done with the Bioconductor package ggbio.
  For example this plot was made w/ ggbio:
  
  I think ggbio is great for making fast plots, but it is kind of a pain to make publication quality figures because it is hard to manipulate the plots after they are constructed. I have noticed that ggbio constructed plots do not return a ggplot or gtable object. I can access the grobs from a ggbio tracks object, but they won't be aligned as they would be in the final plot. I like to make shared legends and include them in the plot, and without having a gtable or ggplot object it is hard to add the shared legends in, and I end up having to save them as a separate file which is tedious. FYI I mainly use ggbio for gene tracks + x-axis alignment.
  Normally I would use ggplot2 to customize the plot extensively, but it doesn't work very well after plotting with ggbio.
  How can I use ggbio for these types of plots? Is there a better tool instead?
  EDIT:
  I would get the legends for each individual plot and combine them before adding back to the main plot using something like this:
  leg1.grob = cowplot::get_legend(plot1)
  leg2.grob = cowplot::get_legend(plot2)
  sharedleg.grob = gridExtra::gtable_rbind(leg1,leg2)
  
  mainplot.grob = ggplotGrob(main_plot)
  mainplot.grob = gtable_add_cols(mainplot.grob, 
                                  widths = sum(sharedleg.grob$widths))
  mainplot.grob = gtable_add_grobs(mainplot.grob, 
                                   grobs = sharedleg.grob, 
                                   t=t,b=b,l=l,r=r)
  cowplot::ggdraw(mainplot.grob)
  
  I've used gviz before, but long ago. Do you know if you can extract the grid plot grobs of the gene track plots with Gviz?
  
  user10434: It seems like the ggbio object already contains the corresponding ggplot object.
  You should be able to extract the ggplot from the ggbio_obj@ggplot slot and extract your gtables.
  Small example:
  library(ggbio)
  library(ggplot2)
  library(patchwork)
  
  fl.bam <- system.file("extdata", "wg-brca1.sorted.bam", package = "biovizBase")
  wh <- as(c("chr17:41239394-41319151:+"), "GRanges")
  p_cov <- autoplot(fl.bam, which = wh)
  #> reading in as Bamfile
  #> Parsing raw coverage...
  #> Read GAlignments from BamFile...
  #> extracting information...
  
  
  df <-  data.frame(x= sample(1:100,size = 100,replace = TRUE))
  p_hist <- ggplot(df) + 
      geom_histogram(aes(x))
  
  
  p_hist / p_cov
  #> Error: Can't add `e2` to a ggplot object.
  #> Backtrace:
  #>     █
  #>  1. └─patchwork:::`/.ggplot`(p_hist, p_cov)
  #>  2.   └─GGally:::`+.gg`(e1, e2)
  #>  3.     └─e1 %+% e2
  #>  4.       └─ggplot2:::add_ggplot(e1, e2, e2name)
  #>  5.         ├─ggplot2::ggplot_add(object, p, objectname)
  #>  6.         └─ggplot2:::ggplot_add.default(object, p, objectname)
  
  p_hist / p_cov@ggplot
  #> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
  
  
  Created on 2021-02-05 by the reprex package (v1.0.0)
  Session info
  
  sessioninfo::session_info()
  #> ─ Session info ───────────────────────────────────────────────────────────────
  #>  setting  value                       
  #>  version  R version 3.6.0 (2019-04-26)
  #>  os       Red Hat Enterprise Linux    
  #>  system   x86_64, linux-gnu           
  #>  ui       X11                         
  #>  language (EN)                        
  #>  collate  en_US.UTF-8                 
  #>  ctype    en_US.UTF-8                 
  #>  tz       Europe/Berlin               
  #>  date     2021-02-05                  
  #> 
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  #>  acepack                1.4.1    2016-10-29 [1] CRAN (R 3.6.0)
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original_text: 39: <p>I am curious how people make genome track plots such as what can be done with the Bioconductor package <code>ggbio</code>.</p>
  <p>For example this plot was made w/ ggbio:
  <a href="https://i.stack.imgur.com/E56jf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/E56jf.png" alt="enter image description here" /></a></p>
  <p>I think ggbio is great for making fast plots, but it is kind of a pain to make publication quality figures because it is hard to manipulate the plots after they are constructed. I have noticed that ggbio constructed plots do not return a ggplot or gtable object. I can access the grobs from a ggbio tracks object, but they won't be aligned as they would be in the final plot. I like to make shared legends and include them in the plot, and without having a gtable or ggplot object it is hard to add the shared legends in, and I end up having to save them as a separate file which is tedious. FYI I mainly use ggbio for gene tracks + x-axis alignment.</p>
  <p>Normally I would use ggplot2 to customize the plot extensively, but it doesn't work very well after plotting with ggbio.</p>
  <p>How can I use ggbio for these types of plots? Is there a better tool instead?</p>
  <p>EDIT:
  I would get the legends for each individual plot and combine them before adding back to the main plot using something like this:</p>
  <pre><code>leg1.grob = cowplot::get_legend(plot1)
  leg2.grob = cowplot::get_legend(plot2)
  sharedleg.grob = gridExtra::gtable_rbind(leg1,leg2)
  
  mainplot.grob = ggplotGrob(main_plot)
  mainplot.grob = gtable_add_cols(mainplot.grob, 
                                  widths = sum(sharedleg.grob$widths))
  mainplot.grob = gtable_add_grobs(mainplot.grob, 
                                   grobs = sharedleg.grob, 
                                   t=t,b=b,l=l,r=r)
  cowplot::ggdraw(mainplot.grob)
  </code></pre>
  <p>I've used <a href="https://bioconductor.org/packages/release/bioc/html/Gviz.html" rel="nofollow noreferrer">gviz</a> before, but long ago. Do you know if you can extract the grid plot grobs of the gene track plots with Gviz?</p>
   10434: <p>It seems like the ggbio object already contains the corresponding ggplot object.
  You should be able to extract the ggplot from the <code>ggbio_obj@ggplot</code> slot and extract your gtables.</p>
  <p>Small example:</p>
  <pre class="lang-r prettyprint-override"><code>library(ggbio)
  library(ggplot2)
  library(patchwork)
  
  fl.bam &lt;- system.file(&quot;extdata&quot;, &quot;wg-brca1.sorted.bam&quot;, package = &quot;biovizBase&quot;)
  wh &lt;- as(c(&quot;chr17:41239394-41319151:+&quot;), &quot;GRanges&quot;)
  p_cov &lt;- autoplot(fl.bam, which = wh)
  #&gt; reading in as Bamfile
  #&gt; Parsing raw coverage...
  #&gt; Read GAlignments from BamFile...
  #&gt; extracting information...
  
  
  df &lt;-  data.frame(x= sample(1:100,size = 100,replace = TRUE))
  p_hist &lt;- ggplot(df) + 
      geom_histogram(aes(x))
  
  
  p_hist / p_cov
  #&gt; Error: Can't add `e2` to a ggplot object.
  #&gt; Backtrace:
  #&gt;     █
  #&gt;  1. └─patchwork:::`/.ggplot`(p_hist, p_cov)
  #&gt;  2.   └─GGally:::`+.gg`(e1, e2)
  #&gt;  3.     └─e1 %+% e2
  #&gt;  4.       └─ggplot2:::add_ggplot(e1, e2, e2name)
  #&gt;  5.         ├─ggplot2::ggplot_add(object, p, objectname)
  #&gt;  6.         └─ggplot2:::ggplot_add.default(object, p, objectname)
  
  p_hist / p_cov@ggplot
  #&gt; `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
  </code></pre>
  <p><img src="https://i.imgur.com/iob3cXO.png" alt="" /></p>
  <p><sup>Created on 2021-02-05 by the <a href="https://reprex.tidyverse.org" rel="nofollow noreferrer">reprex package</a> (v1.0.0)</sup></p>
  Session info
  <p>
  <pre class="lang-r prettyprint-override"><code>sessioninfo::session_info()
  #&gt; ─ Session info ───────────────────────────────────────────────────────────────
  #&gt;  setting  value                       
  #&gt;  version  R version 3.6.0 (2019-04-26)
  #&gt;  os       Red Hat Enterprise Linux    
  #&gt;  system   x86_64, linux-gnu           
  #&gt;  ui       X11                         
  #&gt;  language (EN)                        
  #&gt;  collate  en_US.UTF-8                 
  #&gt;  ctype    en_US.UTF-8                 
  #&gt;  tz       Europe/Berlin               
  #&gt;  date     2021-02-05                  
  #&gt; 
  #&gt; ─ Packages ───────────────────────────────────────────────────────────────────
  #&gt;  package              * version  date       lib source        
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  </code></pre>
  </p>
  
  