Usage
COBRAPlot(fdrtpr = data.frame(), fdrtprcurve = data.frame(),
fdrnbr = data.frame(), corr = data.frame(), fdrnbrcurve = data.frame(),
tpr = data.frame(), fpr = data.frame(), roc = data.frame(),
scatter = data.frame(), onlyshared = NA, fpc = data.frame(),
overlap = data.frame(), plotcolors = "", splv = "",
deviation = data.frame(), maxsplit = NA_integer_, facetted = NA)
Arguments
fdrtpr
A data frame containing observed FDR and TPR values at various
adjusted p-value thresholds.
fdrtprcurve
A data frame containing observed FDR and TPR values for a
(potentially large) number of cutoffs applied to a 'score' (that can be
p-value, adjusted p-value or a more general score).
fdrnbr
A data frame containing observed FDR and the number of features
considered to be significant at various adjusted p-value thresholds.
corr
A data frame containing observed (Pearson and Spearman)
correlation values between observed and true scores.
fdrnbrcurve
A data frame containing observed FDR and number of
features considered to be significant for a (potentially large) number of
cutoffs applied to a 'score' (that can be p-value, adjusted p-value or a
more general score).
tpr
A data frame containing observed TPR values at various adjusted
p-value thresholds.
fpr
A data frame containing observed FPR values at various adjusted
p-value thresholds.
roc
A data frame containing observed FPR and TPR values for a
(potentially large) number of cutoffs applied to a 'score' (that can be
p-value, adjusted p-value or a more general score), which can be used to
generate a ROC curve.
scatter
A data frame containing observed 'scores' (p-values, adjusted
p-values or more general scores) and true scores, which can be used to
generate scatter plots.
onlyshared
A logical value indicating whether only features shared
between the results and the truth should be retained, or if all features
present in the truth should be used.
fpc
A data frame containing observed numbers of false positive
findings among the N top-ranked features (ranked by p-values, adjusted
p-values or more general scores), for a (potentially large) number of Ns,
which can be used to generate a false positive curve.
overlap
A data frame or list of data frames with binary values
indicating, for each of a number of methods and number of features, whether
the method consider the feature 'positive' (significant, 1) or 'negative'
(non-significant, 0). If it is a list of data frames, each list element
corresponds to one level of a stratifying factor.
plotcolors
A character vector giving the color for each method (or
method-stratification level combination).
splv
A character string giving the name of the stratification factor,
"none" if the results are not stratified.
deviation
A data frame containing deviations between observed scores
and true scores.
maxsplit
A numeric value indicating the largest number of levels to
retain if the results have been stratified by an annotation.
facetted
A logical indicating whether the data is prepared for a
facetted plot (separating different stratification levels into different
panels) or for displaying all values in one plot panel.