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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggmaplot.R
\name{ggmaplot}
\alias{ggmaplot}
\title{MA-plot from means and log fold changes}
\usage{
ggmaplot(
data,
fdr = 0.05,
fc = 1.5,
genenames = NULL,
detection_call = NULL,
size = NULL,
alpha = 1,
seed = 42,
font.label = c(12, "plain", "black"),
label.rectangle = FALSE,
palette = c("#B31B21", "#1465AC", "darkgray"),
top = 15,
select.top.method = c("padj", "fc"),
label.select = NULL,
main = NULL,
xlab = "Log2 mean expression",
ylab = "Log2 fold change",
ggtheme = theme_classic(),
...
)
}
\arguments{
\item{data}{an object of class DESeqResults, get_diff, DE_Results, matrix or
data frame containing the columns baseMean (or baseMeanLog2),
log2FoldChange, and padj. Rows are genes.
Two possible formats are accepted for the input data: \itemize{ \item 1/
\code{baseMean | log2FoldChange | padj}. This is a typical output from
DESeq2 pipeline. Here, we'll use log2(baseMean) as the x-axis variable.
\item 2/ \code{baseMeanLog2 | log2FoldChange | padj}. Here, baseMeanLog2 is
assumed to be the mean of logged values; so we'll use it as the x-axis
variable without any transformation. This is the real A in MA plot. In other
words, it is the average of two log-scales values: \code{A = (log2(x) +
log2(y))/2 = log2(xy)*1/2} }
Terminology:
\itemize{ \item baseMean: the mean expression of genes in the two groups.
\item log2FoldChange: the log2 fold changes of group 2 compared to group 1
\item padj: the adjusted p-value of the used statiscal test. }}
\item{fdr}{Accepted false discovery rate for considering genes as
differentially expressed.}
\item{fc}{the fold change threshold. Only genes with a fold change >= fc and
padj <= fdr are considered as significantly differentially expressed.}
\item{genenames}{a character vector of length nrow(data) specifying gene names
corresponding to each row. Used for point labels.}
\item{detection_call}{a numeric vector with length = nrow(data), specifying if
the genes is expressed (value = 1) or not (value = 0). For example
detection_call = c(1, 1, 0, 1, 0, 1). Default is NULL. If detection_call
column is available in data, it will be used.}
\item{size}{points size.}
\item{alpha}{numeric value betwenn 0 an 1 specifying point alpha for
controlling transparency. For example, use alpha = 0.5.}
\item{seed}{Random seed passed to \code{set.seed}. if
\code{NA}, set.seed will not be called. Default is 42 for reproducibility.}
\item{font.label}{a vector of length 3 indicating respectively the size (e.g.:
14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the
color (e.g.: "red") of point labels. For example \emph{font.label = c(14,
"bold", "red")}.}
\item{label.rectangle}{logical value. If TRUE, add rectangle underneath the
text, making it easier to read.}
\item{palette}{the color palette to be used for coloring or filling by groups.
Allowed values include "grey" for grey color palettes; brewer palettes e.g.
"RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and
scientific journal palettes from ggsci R package, e.g.: "npg", "aaas",
"lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".}
\item{top}{the number of top genes to be shown on the plot. Use top = 0 to
hide to gene labels.}
\item{select.top.method}{methods to be used for selecting top genes. Allowed
values include "padj" and "fc" for selecting by adjusted p values or fold
changes, respectively.}
\item{label.select}{character vector specifying some labels to show.}
\item{main}{plot main title.}
\item{xlab}{character vector specifying x axis labels. Use xlab = FALSE to
hide xlab.}
\item{ylab}{character vector specifying y axis labels. Use ylab = FALSE to
hide ylab.}
\item{ggtheme}{function, ggplot2 theme name. Default value is theme_pubr().
Allowed values include ggplot2 official themes: theme_gray(), theme_bw(),
theme_minimal(), theme_classic(), theme_void(), ....}
\item{...}{other arguments to be passed to \code{\link{ggpar}}.}
}
\value{
returns a ggplot.
}
\description{
Make MA-plot which is a scatter plot of log2 fold changes (M, on
the y-axis) versus the average expression signal (A, on the x-axis). \code{M
= log2(x/y)} and \code{A = (log2(x) + log2(y))/2 = log2(xy)*1/2}, where x
and y are respectively the mean of the two groups being compared.
}
\examples{
data(diff_express)
# Default plot
ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"),
fdr = 0.05, fc = 2, size = 0.4,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(diff_express$name),
legend = "top", top = 20,
font.label = c("bold", 11),
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
# Add rectangle around labels
ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"),
fdr = 0.05, fc = 2, size = 0.4,
palette = c("#B31B21", "#1465AC", "darkgray"),
genenames = as.vector(diff_express$name),
legend = "top", top = 20,
font.label = c("bold", 11), label.rectangle = TRUE,
font.legend = "bold",
font.main = "bold",
ggtheme = ggplot2::theme_minimal())
# Select specific genes to show
# set top = 0, then specify genes using label.select argument
ggmaplot(diff_express, main = expression("Group 1" \%->\% "Group 2"),
fdr = 0.05, fc = 2, size = 0.4,
genenames = as.vector(diff_express$name),
ggtheme = ggplot2::theme_minimal(),
top = 0, label.select = c("BUB1", "CD83")
)
}
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