plots: Plotting the features with differential abundance.

In permubiome: A Permutation Based Test for Biomarker Discovery in Microbiome Data

Plotting the features with differential abundance.

Description

Option to plot individually all features found to be differentially presented in the classes of your dataset.

Usage

plots()

Details

When executed, the name of the feature as well as the different output options will be prompted.

Author(s)

Alfonso Benitez-Paez

References

Benitez-Paez A. 2023. Permubiome: an R package to perform permutation based test for biomarker discovery in microbiome analyses. [https://cran.r-project.org]. Benitez-Paez A, et al. mSystems. 2020;5:e00857-19. doi: 10.1128/mSystems.00857-19.

Examples

## The function is currently defined as
function () 
{
    Class <- NULL
    non_zero <- NULL
    Occurring <- NULL
    prevalence <- NULL
    ref<-NULL
    loadNamespace("ggplot2")
    loadNamespace("Matrix")
    load("permubiome.RData")
    df_norm <- df_norm
    category <- readline("Type the category you want to plot : ")
    if (category == "") {
        category <- colnames(df_norm[3])
        print(paste("As you declared no categories, the very first one of your 
        dataset is plotted!"))
    }
    df_to_plot <- df_norm[, c("Sample", "Class", category)]
    classes <- levels(df_to_plot$Class)
    if (REFERENCE == "") {
        REFERENCE <- classes[1]
    }
    else if (REFERENCE == classes[2]) {
        classes[2] <- classes[1]
        classes[1] <- REFERENCE
    }
    df_to_plot$ref <- factor(df_to_plot$Class, levels = (c(classes[1], 
        classes[2])))
    p1 <- (ggplot(df_to_plot, aes(ref, df_to_plot[, 
        category], fill = Class), environment = environment())) + 
        geom_boxplot(notch = F, outlier.colour = "blue", outlier.shape = 1, 
            outlier.size = 3) + geom_point(colour = "#000000", 
        size = 2.5, pch = 19) + scale_fill_manual(values = c("#E41A1C", 
        "#377EB8")) + ggtitle(category) + guides(fill = FALSE) + 
        theme(plot.title = element_text(size = 24, face = "bold")) + 
        ylab("Normalized read proportion") + 
        xlab("Classes") + theme(axis.text = element_text(size = 12), 
        axis.title = element_text(size = 16, face = "bold")) + 
        coord_flip() + theme(plot.margin = unit(c(0.25, 
        0.25, 0.25, 0.25), "cm"))
    non_zero <- as.data.frame((tapply(df_to_plot[[category]], 
        df_to_plot$ref, nnzero)))
    total <- as.data.frame((tapply(df_to_plot[[category]], df_to_plot$ref, 
        length)))
    prevalence_table <- data.frame(names = factor(c(classes[1], 
        classes[2]), levels = c(classes[1], classes[2])), Occurring = c(non_zero[, 
        1]), Subjects = c(total[, 1]))
    prevalence_table$prevalence <- (prevalence_table$Occurring/prevalence_table$Subjects) * 
        100
    p2 <- ggplot(prevalence_table, aes(x = names, y = prevalence, fill = names, 
        width = 0.75)) + geom_bar(stat = "identity", colour = "grey20") + 
        scale_fill_manual(values = c("#E41A1C", "#377EB8")) + 
        coord_flip() + guides(fill = FALSE) + ggtitle(paste("", 
        "", sep = " ")) + theme(plot.title = element_text(size = 24, 
        face = "bold")) + ylab("Prevalence (percentage)") + theme(axis.title.y = element_blank(), 
        axis.text.y = element_blank(), axis.text.x = element_text(size = 12), 
        axis.title = element_text(size = 16, face = "bold")) + 
        theme(plot.margin = unit(c(0.25, 0.25, 0.25, 0.25), "cm")) + 
        ylim(0, 100)
    output <- readline("Do you want an output file (yes/no)? : ")
    if (substr(output, 1, 1) == "y") {
        extension <- readline("What extension do you prefer for the output plot 
        (ps, pdf, jpeg, tiff, png, bmp )? : ")
        tiff(filename = paste(category, extension, sep = "."), 
            width = 1000, height = 200, res = 100, units = "px")
        grid.arrange(p1, p2, ncol = 2)
        dev.off()
    }
    else {
        print(grid.arrange(p1, p2, ncol = 2))
    }
    save(df, df_norm, df_to_plot, REFERENCE, classes, file = "permubiome.RData")
  }

permubiome documentation built on Oct. 16, 2023, 5:06 p.m.