In this vignette, we will build a shiny app to visualize RangeSummarizedExperiment using epivizrChart. Since epiviz visualization library is built upon the web components framework, it can be integrated with most frameworks that support HTML.

Sample data sets to use for the vignette.


We create an Environment element which visualizes genome wide data. We then visualize cancer and normal values from the SummarizedExperiment object.

epivizEnv <- epivizEnv(interactive = TRUE)
scatterplot <- epivizEnv$plot(sumexp, datasource_name="sumExp", columns=c("cancer", "normal"))

After looking at the genomic wide data, if you are interested in further exploring a specific region of the genome, We can create a navigation element linked to that genomic location. We can plot additional annotation/data charts/tracks in this region.

epivizNav <- epivizNav(chr="chr11", start=118000000, end=121000000, parent=epivizEnv, interactive = TRUE)

genes_track <- epivizNav$add_genome(Homo.sapiens, datasource_name="genes")
## creating gene annotation (it may take a bit)
##   403 genes were dropped because they have exons located on both strands
##   of the same reference sequence or on more than one reference sequence,
##   so cannot be represented by a single genomic range.
##   Use 'single.strand.genes.only=FALSE' to get all the genes in a
##   GRangesList object, or use suppressMessages() to suppress this message.
## 'select()' returned 1:1 mapping between keys and columns
# region_scatterplot <- epivizNav$plot(sumexp, datasource_name="sumExp", columns=c( "cancer", "normal"))
region_linetrack <- epivizNav$plot(sumexp, datasource_name="sumExp", columns=c( "cancer", "normal"), chart="LineTrack")

Finally, we can embed these components in a Shiny App.

app <- shinyApp(
  server=function(input, output, session) {
    output$epivizChart <- renderUI({