bigPint 1.2.2
Interactive plots in the bigPint package open as Shiny applications. These applications consist of a simple dashboard that includes an “About” tab that explains how to use the application. They also include an “Application” tab that offers several input fields that the user can tailor to their needs. Some of these input fields are generated dynamically based off the dataset that the user input. These tailorizations include aesthetics (such as point color) and metrics to determine the subset of data to be superimposed. In these applications, users can also easily save static images of the interactive graphics and lists of selected genes to their local computer.
Currently, there are four interactive plots available in the bigPint package:
Below we provide examples on how to produce these types of plots.
Interactive scatterplot matrices can be generated with the bigPint function plotSMApp(). The code below will generate an example interactive scatterplot matrix from the soybean cotyledon dataset (Brown and Hudson 2015).
library(bigPint)
data("soybean_cn_sub")
soybean_cn_sub <- soybean_cn_sub[,1:7]
app <- plotSMApp(data=soybean_cn_sub)
if (interactive()) {
shiny::runApp(app)
}Upon running the code above in your R session, an interactive window will open. By default, you will first see an “About” tab that explains the application. Simply click on the “Application” tab under the navigation icon at the top of the screen to enter the actual interactive graphic. Embedded below is the resulting application (which can also be accessed directly here).
Interactive litre plots can be generated with the bigPint function plotLitreApp(). The code below will generate an example interactive litre plot using the soybean iron metabolism dataset after being logged (Lauter and Graham 2016).
data("soybean_ir_sub")
data("soybean_ir_sub_metrics")
soybean_ir_sub_log <- soybean_ir_sub
soybean_ir_sub_log[,-1] <- log(soybean_ir_sub[,-1]+1)
app <- plotLitreApp(data=soybean_ir_sub_log,
dataMetrics = soybean_ir_sub_metrics)
if (interactive()) {
shiny::runApp(app, port = 1234, launch.browser = TRUE)
}Upon running the code above in your R session, an interactive window will open. By default, you will first see an “About” tab that explains the application. Simply click on the “Application” tab under the navigation icon at the top of the screen to enter the actual interactive graphic. Embedded below is the resulting application (which can also be accessed directly here).
Users can produce interactive parallel coordinate plots with the bigPint function plotPCPApp(). The code below will generate an example interactive parallel coordinate plot using the soybean iron metabolism dataset after being standardized (Lauter and Graham 2016).
soybean_ir_sub_st = as.data.frame(t(apply(as.matrix(soybean_ir_sub[,-1]), 1,
scale)))
soybean_ir_sub_st$ID = as.character(soybean_ir_sub$ID)
soybean_ir_sub_st = soybean_ir_sub_st[,c(length(soybean_ir_sub_st),
1:length(soybean_ir_sub_st)-1)]
colnames(soybean_ir_sub_st) = colnames(soybean_ir_sub)
nID = which(is.nan(soybean_ir_sub_st[,2]))
soybean_ir_sub_st[nID,2:length(soybean_ir_sub_st)] = 0
plotGenes = filter(soybean_ir_sub_metrics[["N_P"]], FDR < 0.01, logFC < -4) %>%
select(ID)
pcpDat = filter(soybean_ir_sub_st, ID %in% plotGenes[,1])
app <- plotPCPApp(data = pcpDat)
if (interactive()) {
shiny::runApp(app, display.mode = "normal")
}Upon running the code above in your R session, an interactive window will open. By default, you will first see an “About” tab that explains the application. Simply click on the “Application” tab under the navigation icon at the top of the screen to enter the actual interactive graphic. Embedded below is the resulting application (which can also be accessed directly here).
Users can produce interactive parallel coordinate plots with the bigPint function plotVolcanoApp(). The code below will generate an example interactive volcano plot using the soybean iron metabolism dataset after being logged (Lauter and Graham 2016).
app <- plotVolcanoApp(data = soybean_ir_sub_log,
dataMetrics = soybean_ir_sub_metrics)
if (interactive()) {
shiny::runApp(app)
}Upon running the code above in your R session, an interactive window will open. By default, you will first see an “About” tab that explains the application. Simply click on the “Application” tab under the navigation icon at the top of the screen to enter the actual interactive graphic. Embedded below is the resulting application (which can also be accessed directly here).
Brown, Anne V., and Karen A. Hudson. 2015. “Developmental Profiling of Gene Expression in Soybean Trifoliate Leaves and Cotyledons.” BMC Plant Biology 15 (1). BioMed Central:169.
Lauter, AN Moran, and MA Graham. 2016. “NCBI Sra Bioproject Accession: PRJNA318409.”