plotPCPApp {bigPint} | R Documentation

## Plot interactive parallel coordinate plots

### Description

Plot interactive parallel coordinate plots.

### Usage

    plotPCPApp(data = data, dataSE = NULL, pointColor = "orange")

### Arguments

`data` | DATA FRAME | Read counts for parallel coordinate lines
`dataSE` | SUMMARIZEDEXPERIMENT | Summarized experiment format that can be used in lieu of data; default NULL
`pointColor` | CHARACTER STRING | Color of overlaid points on scatterplot matrix; default "orange"

### Value

A Shiny application that shows a parallel coordinate plot and allows users to draw rectangular areas across samples and remove genes that are not inside these areas. The user can download a file that contains the gene IDs that remain.

### Examples

    # The first example uses data and dataMetrics objects as
    # input. The last example creates the same plot now using the
    # SummarizedExperiment (i.e. dataSE) object input.

    # Example: Create interactive parallel coordinate plot for genes that have
    # FDR < 0.01 and logFC < -4. Standardize genes to have an average of zero
    # and a standard deviation of one.

    data(soybean_ir_sub)
    data(soybean_ir_sub_metrics)

    # Create standardized version of data
    library(matrixStats)
    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

    library(dplyr, warn.conflicts = FALSE)
    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, pointColor = "purple")
    if (interactive()) {
        shiny::runApp(app, display.mode = "normal")
    }

    # Below is the same example, only now using the
    # SummarizedExperiment (i.e. dataSE) object as input.

    # Example: Create interactive parallel coordinate plot for genes that have
    # FDR < 0.01 and logFC < -4. Standardize genes to have an average of zero
    # and a standard deviation of one.

    ## Not run:
    data(se_soybean_ir_sub)

    # Create standardized version of data
    library(matrixStats)
    se_soybean_ir_sub_st = se_soybean_ir_sub
    assay(se_soybean_ir_sub_st) <-as.data.frame(t(apply(as.matrix(as.data.frame(
        assay(se_soybean_ir_sub))), 1, scale)))
    nID <- which(is.nan(as.data.frame(assay(se_soybean_ir_sub_st))[,1]))
    assay(se_soybean_ir_sub_st)[nID,] <- 0

    # To subset our SummarizedExperiment data by a list of genes, we can
    # invoke the convertSESubsetGenes() method.

    library(dplyr, warn.conflicts = FALSE)
    geneList <- as.data.frame(rowData(se_soybean_ir_sub_st)) %>%
        filter(N_P.FDR <= 0.01) %>% filter(N_P.logFC < -4)
    geneList <- geneList[,1]
    pcpDat <- convertSESubsetGenes(se_soybean_ir_sub_st, geneList)

    app <- plotPCPApp(dataSE = pcpDat, pointColor = "purple")
    if (interactive()) {
        shiny::runApp(app, display.mode = "normal")
    }

    ## End(Not run)

[Package bigPint version 1.4.0 Index]