R/oncoMix_examples.R

In dpique/oncomix: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data

#' Creating an ideal oncomix gene
#'
#' This function allows you to generate a plot
#' @param means Set the difference between parameter means for the
#' overexpressed (oe) group. Defaults to c(3,7)
#' @keywords oncoMix, idealized, theoretical
#' @return Returns a ggplot object that shows the statistical model for an
#' idealized/theoretical oncogene candidate mRNA that is overexpressed in a
#' subset of tumors
#' @export
#' @examples
#' oncoMixIdeal(means=c(3,10))
#' oncoMixIdeal(means=c(2,18.5))

oncoMixIdeal <- function(means=c(3,7)){
    df3 <- data.frame(cbind("expr"=c(rnorm(113, 3), c(rnorm(56,3),
        rnorm(57, mean=6)))), "type"=as.factor(c(rep(2, 113), rep(1,113))))
    type <- expr <- NULL
    ggplot(df3, aes(x=expr, color=type, fill=type, group=type)) +
    theme_classic() +
    theme(
        axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        legend.position="none",
        plot.title=element_text(size=12),
        axis.text=element_text(size=8),
        axis.title=element_text(size=8),
        axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()
        ) +
    ggtitle("Theoretical") +
    xlim(means[1]-3.2,means[2]+3) +
    stat_function(fun="dnorm",
        colour="#F8766D",
        args=list(mean=3.1, sd=0.7),
        size=3) +
    stat_function(fun="dnorm",
        colour="#F8766D",
        args=list(mean=2.9, sd=0.7),
        size=3) +
    stat_function(fun="dnorm",
        colour="#00BFC4",
        args=list(mean=means[1], sd=1),
        size=3) +
    stat_function(fun="dnorm",
        colour="#00BFC4",
        args=list(mean=means[2], sd=1),
        size=3)
}

#' Creating a schematic of a 2-component mixture model
#'
#' This function allows you to generate a plot
#' @param means Set the values for the difference between parameter means
#' @keywords oncoMix, idealized, theoretical
#' @return Returns a ggplot object that shows a 2-component Gaussian mixture
#' model
#' @export
#' @examples
#' oncoMixBimodal(means=c(3,7))
#' oncoMixBimodal(means=c(3,10))

oncoMixBimodal <- function(means=c(3,7)){
    d1 = data.frame(cbind("expr"=c(rnorm(113, 3), c(rnorm(56,3),
        rnorm(57, mean =6)))), "type"=as.factor(c(rep(2, 113), rep(1,113))))
    type <- expr <- NULL
    ggplot(d1, aes(x=expr, color=type, fill=type, group=type)) +
        theme_classic() +
        theme(
            axis.title.y=element_blank(),
            axis.text.y=element_blank(),
            axis.ticks.y=element_blank(),
            legend.position="none",
            plot.title=element_text(size=12),
            axis.text=element_text(size=8),
            axis.title=element_text(size=8),
            axis.title.x=element_blank(),
            axis.text.x=element_blank(),
            axis.ticks.x=element_blank()) +
        ggtitle("Theoretical") + xlim(-0.2,10) +
        stat_function(fun=dnorm,
            colour="#00BFC4",
            args=list(mean=means[1], sd=1),
            size=5) +
        stat_function(fun=dnorm,
            colour="#00BFC4",
            args=list(mean=means[2], sd=1),
            size=5)
}

#' Creating a schematic of a traditional diff. expression experiment
#'
#' This function allows you to generate a schematic of the assumptions of a
#' traditional DE expermiment between two known groups.
#' @param means Set the values for the difference between parameter means
#' @keywords oncoMix, idealized, theoretical, differential expression
#' @return Returns a ggplot object that shows the traditional method (2 sample
#' t-test) for mRNA differential expression.
#' @export
#' @examples
#' oncoMixTraditionalDE(means=c(3,7))
#' oncoMixTraditionalDE(means=c(3,10))

oncoMixTraditionalDE <- function(means=c(3,7)){
    d2 <- data.frame(cbind("expr"=c(rnorm(113, 3),
        c(rnorm(56,3), rnorm(57, mean=6)))),
        "type"=as.factor(c(rep(2, 113), rep(1,113))))
    type <- expr <- NULL
    ggplot(d2, aes(x=expr, color=type, fill=type, group=type)) +
        theme_classic() +
        theme(
            axis.title.y=element_blank(),
            axis.text.y=element_blank(),
            axis.ticks.y=element_blank(),
            legend.position="none",
            plot.title=element_text(size=12),
            axis.text=element_text(size=8),
            axis.title=element_text(size=8),
            axis.title.x=element_blank(),
            axis.text.x=element_blank(),
            axis.ticks.x=element_blank()) +
        ggtitle("Theoretical") + xlim(-0.2,10) +
        stat_function(fun="dnorm",
            colour="#F8766D",
            args=list(mean=means[1], sd=1),
            size=5) +
        stat_function(fun="dnorm",
            colour="#00BFC4",
            args=list(mean=means[2], sd=1),
            size=5)
}