| MAplot | R Documentation |
This function plots log2 fold changes (y-axis) versus the mean of normalized counts (on the x-axis). Statistically significant features are colored.
MAplot( degseqDF, FDR.cutoff = 0.05, comparison, filter = c(Fold = 2, FDR = 10), genes = "NULL", plotly = FALSE, savePlot = FALSE, filePlot = NULL )
degseqDF |
object of class |
FDR.cutoff |
filter cutoffs for the p-value adjusted. |
comparison |
|
filter |
Named vector with filter cutoffs of format c(Fold=2, FDR=1) where Fold refers to the fold change cutoff (unlogged) and FDR to the p-value cutoff. |
genes |
|
plotly |
logical: when |
savePlot |
logical: when |
filePlot |
file name where the plot will be saved. For more information,
please consult the |
returns an object of ggplot or plotly class.
## Load targets file and count reads dataframe
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment = "#")
cmp <- systemPipeR::readComp(
file = targetspath, format = "matrix",
delim = "-"
)
countMatrixPath <- system.file("extdata", "countDFeByg.xls",
package = "systemPipeR"
)
countMatrix <- read.delim(countMatrixPath, row.names = 1)
### DEG analysis with `systemPipeR`
degseqDF <- systemPipeR::run_DESeq2(
countDF = countMatrix, targets = targets,
cmp = cmp[[1]], independent = FALSE
)
DEG_list <- systemPipeR::filterDEGs(
degDF = degseqDF,
filter = c(Fold = 2, FDR = 10)
)
## Plot
MAplot(degseqDF,
comparison = "M12-A12", filter = c(Fold = 1, FDR = 20),
genes = "ATCG00280"
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.