makeBoxplot {BEclear} | R Documentation |
A simple boxplot
is done with boxes either separated
by batches or by samples and describe the five number summary of all beta
values corresponding to a batch or a sample, respectively. The batch_ids are
shown on the x-axis with a coloring corresponding to the BEscore.
makeBoxplot(data, samples, score, bySamples=FALSE, col="standard", main="", xlab="Batch", ylab="Beta value", scoreCol=TRUE)
data |
any matrix filled with beta values, column names have to be sample_ids corresponding to the ids listed in "samples", row names have to be gene names. |
samples |
data frame with two columns, the first column has to contain the sample numbers, the second column has to contain the corresponding batch number. Colnames have to be named as "sample_id" and "batch_id". |
score |
data frame produced by the |
bySamples |
should the boxes be separated by samples or not. If not, boxes are separated by the batch_ids. |
col |
colors for the boxes, refers to the standard
|
main |
main title for the box plot. Default is an empty string. |
xlab |
label for the x-axis of the box plot. Default is "Batch". |
ylab |
label for the y-axis of the box plot. Default is "Beta value" |
scoreCol |
should the batch_ids on the a-axis be colored according to the BEscore or not? If not, black is used as color for all batch_ids. |
The color code for the batch_ids on the x-axis provides a simple "traffic
light" the user can use to decide if he wants to correct for an assumed batch
effect or not. Green means no batch effect, yellow a possibly existing not
severe batch effect and red stands for an obviously existing batch effect that
should be corrected. The traffic light colors are set according to the BEscore
from the calcScore
function, values from 0 to 0.02 are colored in
green, from 0.02 to 0.1 in yellow and values over 0.1 are colored in red.
Returns a boxplot on the graphic device with the features explained above.
calcScore
boxplot
correctBatchEffect
## Shortly running example. For a more realistic example that takes ## some more time, run the same procedure with the full BEclearData ## dataset. ## Whole procedure that has to be done to use this function. data(BEclearData) ex.data <- ex.data[31:90,7:26] ex.samples <- ex.samples[7:26,] ## Prepare the data for the box plots # Calculate median difference values and p-values meds <- calcMedians(data=ex.data, samples=ex.samples) pvals <- calcPvalues(data=ex.data, samples=ex.samples) # Summarize p-values and median differences for batch affected genes sum <- calcSummary(medians=meds, pvalues=pvals) # Calculate the BEscore for the batch_id colorings of the x-axis score <- calcScore(data=ex.data, samples=ex.samples, summary=sum) ## Simple boxplot for the example data separated by batch makeBoxplot(data=ex.data, samples=ex.samples, score=score, bySamples=FALSE, main="Some box plot") ## Simple boxplot for the example data separated by samples makeBoxplot(data=ex.data, samples=ex.samples, score=score, bySamples=TRUE, main="Some box plot")