Visualizing Epiviz Web Components with epivizrChart

In epivizrChart: R interface to epiviz web components

The epivizrChart package is used to add interactive charts and dashboards for genomic data visualization into RMarkdown and HTML documents using the epiviz framework. It provides an API to interactively create and manage web components that encapsulate epiviz charts. Charts can be embedded in R markdown/notebooks to create interactive documents. Epiviz Web components are built using the Google Polymer library. This vignette demonstrates how to use these visualization components in RMarkdown documents.

library(epivizrChart)
library(antiProfilesData)
library(SummarizedExperiment)
library(RColorBrewer)
library(Homo.sapiens)
library(AnnotationHub)
library(GenomicRanges)

Sample data sets we will be using for the vignette.

data(tcga_colon_blocks)
data(tcga_colon_curves)
data(tcga_colon_expression)
data(apColonData)

What are Epiviz Web Components ?

We currently have three different web components built for genomic data exploration and visualization.

Epiviz Charts

Epiviz charts are used to visualize genomic data objects in R/BioConductor. The data objects can be BioConductor data types for ex: Genomic Ranges, ExpressionSet, SummarizedExperiment etc.

For example, to visualize hg19 reference genome as a genes track at a particular genomic location (chr, start, end)

library(Homo.sapiens)

genes_track <- epivizChart(Homo.sapiens, chr="chr11", start=118000000, end=121000000)
genes_track

epivizChart infers the chart type from the data object that was passed. Instead of inferring a chart type from the data object, we can use the chart parameter to specify a chart type. Currently, we support the following chart types - BlocksTrack, HeatmapPlot, LinePlot, LineTrack, ScatterPlot, StackedLinePlot, StackedLineTrack.

scatter_plot <- epivizChart(tcga_colon_curves, chr="chr11", start=99800000, end=103383180, type="bp", columns=c("cancerMean","normalMean"), chart="ScatterPlot")
scatter_plot

Epiviz Environment

An important part of the epivizrChart design is that data and plots are separated: you can make multiple charts from the same data object without having to replicate data multiple times. This way, data queries are made by data object, not per chart, which leads to a more responsive design of the system. To enable this, we built the epiviz-environment web component. The environment element also enables brushing across all the charts.

To create an environment,

epivizEnv <- epivizEnv(chr="chr11", start=118000000, end=121000000)

genes_track <- epivizEnv$plot(Homo.sapiens)
blocks_track <- epivizEnv$plot(tcga_colon_blocks, datasource_name="450kMeth")

epivizEnv

Epiviz Navigation

epiviz-navigation is an instance of environment with genomic context linked to it. In interactive sessions with a data provider, navigation elements provide functionality to search for a gene/probe and navigate to a genomic location. Navigation elements also provide an ideogram view when collapsed.

To create a navigation,

epivizNav <- epivizNav(chr="chr11", start=118000000, end=121000000)

genes_track <- epivizNav$plot(Homo.sapiens)
blocks_track <- epivizNav$plot(tcga_colon_blocks, datasource_name="450kMeth")

epivizNav

Note: you can create environments without any genomic location. This will then plot all the data from a data object. Navigation elements must be initialized with a genomic location.

epivizrChart examples

We'll walk through a few examples of visualizing different bioconductor data types with epivizrChart and enable interactive data exploration.

First, lets create an epiviz enivornment element

epivizEnv <- epivizEnv(chr="chr11", start=99800000, end=103383180)

Add a genome track to the environment. You can add charts to an environment by using the environment's plot method. For this vignette, we use the human genome from the Homo.sapiens package.

require(Homo.sapiens)

genes_track <- epivizEnv$plot(Homo.sapiens)
genes_track

Add a blocks track using the tcga_colon_blocks object.

blocks_track <- epivizEnv$plot(tcga_colon_blocks, datasource_name="450kMeth")
blocks_track

You can now render the epivizEnv object and see that both the charts are linked to each other. Brushing is now enabled across charts.

epivizEnv

Similarly let's add a line track using the tcga_colon_curves data object. We can specify what columns to visualize from the data object.

means_track <- epivizEnv$plot(tcga_colon_curves, datasource_name="450kMeth", type="bp", columns=c("cancerMean","normalMean"))
means_track

The apColonData object is an ExpressionSet containing gene expression data for colon normal and tumor samples for genes within regions of methylation loss identified this paper.

To visualize an MA plot from the apColonData, we first create an ExpressionSet object and create an EpivizChart object.

keep <- pData(apColonData)$SubType!="adenoma"
apColonData <- apColonData[,keep]
status <- pData(apColonData)$Status
Indexes <- split(seq(along=status),status)

exprMat <- exprs(apColonData)
mns <- sapply(Indexes, function(ind) rowMeans(exprMat[,ind]))
mat <- cbind(colonM=mns[,"1"]-mns[,"0"], colonA=0.5*(mns[,"1"]+mns[,"0"]))

pd <- data.frame(stat=c("M","A"))
rownames(pd) <- colnames(mat)

maEset <- ExpressionSet(
  assayData=mat,
  phenoData=AnnotatedDataFrame(pd),
  featureData=featureData(apColonData),
  annotation=annotation(apColonData)
)

eset_chart <- epivizEnv$plot(maEset, datasource_name="MAPlot", columns=c("colonA","colonM"))
eset_chart

We can also visualize data from SummarizedExperiment objects.

ref_sample <- 2 ^ rowMeans(log2(assay(tcga_colon_expression) + 1))
scaled <- (assay(tcga_colon_expression) + 1) / ref_sample
scaleFactor <- Biobase::rowMedians(t(scaled))
assay_normalized <- sweep(assay(tcga_colon_expression), 2, scaleFactor, "/")
assay(tcga_colon_expression) <- assay_normalized

status <- colData(tcga_colon_expression)$sample_type
index <- split(seq(along = status), status)
logCounts <- log2(assay(tcga_colon_expression) + 1)
means <- sapply(index, function(ind) rowMeans(logCounts[, ind]))
mat <- cbind(cancer = means[, "Primary Tumor"], normal = means[, "Solid Tissue Normal"])

sumexp <- SummarizedExperiment(mat, rowRanges=rowRanges(tcga_colon_expression))

se_chart <- epivizEnv$plot(sumexp, datasource_name="Mean by Sample Type", columns=c("normal", "cancer"))
se_chart

If a data set is already added to an EpivizEnvironment, we can reuse the same data object and visualize the data using a different chart type. This avoids creating multiple copies of data. For example, lets visualize the sumexp using a HeatmapPlot. measurements from different data objects can also be used to create a chart.

# get measurements
measurements <- se_chart$get_measurements()

# create a heatmap using these measurements
heatmap_plot <- epivizEnv$plot(measurements=measurements, chart="HeatmapPlot")
heatmap_plot

If we want to change the ordering of the charts within the EpivizEnvironment, we can use order_charts. Let's reorder the environment and move the HeatmapPlot to the top.

order <- list(
  heatmap_plot,
  genes_track,
  blocks_track,
  means_track,
  se_chart,
  eset_chart
)

epivizEnv$order_charts(order)

Render the Environment and all its charts.

epivizEnv

Navigate the Genome

We can navigate to another location on the genome using the navigate method. This will update the data for all the charts in the environment.

epivizEnv$navigate(chr="chr11", start=110800000, end=130383180)
epivizEnv

Epiviz Navigation Element

Epiviz Navigation elements are useful to visualize data from a particular genomic region. For example, we can create an environment that shows data for an entire chromosome. But a navigation element can then show data for a genomic region. In an interactive session, Navigation elements also provide functionality to search by gene/probe and navigate along the genome(move left/right).

# create an environment to show data from entire chromosome 11
epivizEnv <- epivizEnv(chr="chr11")

# add a line track from tcga_colon_curves object to the environment
means_track <- epivizEnv$plot(tcga_colon_curves, datasource_name="450kMeth", type="bp", columns=c("cancerMean","normalMean"))

# add a scatter plot from the summarized experiment object to the environment
se_chart <- epivizEnv$plot(sumexp, datasource_name="Mean by Sample Type", columns=c("normal", "cancer"))

# create a new navigation element that shows a particular region in chr11
epivizNav <- epivizNav(chr="chr11", start=99800000, end=103383180, parent=epivizEnv)

# add a blocks track to the navigation element
blocks_track <- epivizNav$plot(tcga_colon_blocks, datasource_name="450kMeth")

epivizEnv

If we'd like a navigation element to include all of the current environment's charts at a particular genomic region, we can use the environment's init_region.

epivizEnv <- epivizEnv(chr="chr11")

# add a blocks track to the evironment
blocks_track <- epivizEnv$plot(tcga_colon_blocks, datasource_name="450kMeth")
# add a scatter plot from the summarized experiment object to the environment
se_chart <- epivizEnv$plot(sumexp, datasource_name="Mean by Sample Type", columns=c("normal", "cancer"))

epivizNav <- epivizEnv$init_region(chr="chr11", start=99800000, end=103383180)

epivizEnv

Remove Charts

To remove all the charts from an environment or navigation element, we can use the remove_all_charts methods.

epivizEnv$remove_all_charts()

Create Charts with Settings and Colors

colors <- brewer.pal(3, "Dark2")

blocks_track <- epivizChart(tcga_colon_blocks, chr="chr11", start=99800000, end=103383180, colors=colors)

# to list availble settings for a chart
blocks_track$get_available_settings()

settings <- list(
  title="Blocks",
  minBlockDistance=10
  )

blocks_track$set_settings(settings)
blocks_track

blocks_track$set_colors(c("#D95F02"))
blocks_track

colors <- brewer.pal(3, "Dark2")
lines_track <- epivizChart(tcga_colon_curves, chr="chr11", start=99800000, end=103383180, type="bp", columns=c("cancerMean","normalMean"))
lines_track

lines_track$set_colors(colors)
lines_track

Using Interactive Mode

The interactive mode takes advantage of the websocket protocol to create an active connection between the R-session and the epiviz components visualized in the browser. In interactive mdoe, data is not embedded along with the components, So the charts make data requests to the R-session to get data.

To use charts in interactive mode, first we create an epiviz environment with interactive mode enabled.

library(epivizrChart)

# initialize environment with interactive = true. this argument will init. an epiviz-data-source element
epivizEnv <- epivizEnv(chr="chr11", start=118000000, end=121000000, interactive=TRUE)

We then create an instance of an epivizrServer to manage websocket connections. The register_all_the_epiviz_things adds listeners and handlers to manage data requests.

library(epivizrServer)

library(Homo.sapiens)
data(tcga_colon_blocks)

# initialize server
server <- epivizrServer::createServer()

# register all our actions between websocket and components
epivizrChart:::.register_all_the_epiviz_things(server, epivizEnv)

# start server
server$start_server()

We now have an epiviz environment and an active websocket connection to the R-session. Adding and managing charts is exactly the same as described in this vignette.

# plot charts
blocks_track <- epivizEnv$plot(tcga_colon_blocks, datasource_name="450kMeth")
epivizEnv

genes <- epivizEnv$plot(Homo.sapiens)
epivizEnv

Finally close the server

server$stop_server()

Visualizing data from data.frame

We can visualize genomic data stored in data.frame use epivizrChart. If the data.frame does not contain genomic location columns like chr, start or end, linking between charts is by row_number.

For this example, we will use rna-seq data from AnnotationHub.

ah <- AnnotationHub()
epi <- query(ah, c("roadmap"))
df <- epi[["AH49015"]]

now we'll create a scatter plot to visualize samples "E006" & "E114" from the data.frame

rna_plot <- epivizChart(df, datasource_name="RNASeq", columns=c("E006","E114"), chart="ScatterPlot")
rna_plot

epivizrChart documentation built on Nov. 8, 2020, 8:02 p.m.