makeBoxplot | 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, log = FALSE)
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. |
log |
TRUE, if the y-axis should be on a logarithmic scale. |
makeBoxplot
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 the batch effects
batchEffects <- calcBatchEffects(data = ex.data, samples = ex.samples,
adjusted = TRUE, method = "fdr")
meds <- batchEffects$med
pvals <- batchEffects$pval
## 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"
)
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