This function creates a 'volc3d' object of S4 class for downstream plots
containing the p-values from a three-way group comparison, expression data
sample data and polar coordinates. For RNA-Seq count data, two functions
deseq_polar or voom_polar can be used instead.
polar_coords(
outcome,
data,
pvals = NULL,
padj = pvals,
pcutoff = 0.05,
fc_cutoff = NULL,
scheme = c("grey60", "red", "gold2", "green3", "cyan", "blue", "purple"),
labs = NULL,
...
)Returns an S4 'volc3d' object containing:
'df' A list of 2 dataframes. Each dataframe contains both x,y,z
coordinates as well as polar coordinates r, angle. The first dataframe has
coordinates on scaled data. The 2nd dataframe has unscaled data (e.g. log2
fold change for gene expression). The type argument in
volcano3D, radial_plotly and
radial_ggplot corresponds to these dataframes.
'outcome' The three-group contrast factor used for comparisons
'data' Dataframe or matrix containing the expression data
'pvals' A dataframe containing p-values. First column is the 3-way comparison (LRT or ANOVA). Columns 2-4 are pairwise comparisons between groups A vs B, A vs C and B vs C, where A, B, C are the 3 levels in the outcome factor.
'padj' A dataframe containing p-values adjusted for multiple testing
'pcutoff Numeric value for cut-off for p-value significance
'scheme' Character vector with colour scheme for plotting
'labs' Character vector with labels for colour groups
Outcome vector with 3 groups, ideally as a factor. If it is
not a factor, this will be coerced to a factor. This must have exactly 3
levels. NOTE: if pvals is given, the order of the levels in outcome
must correspond to the order of columns in pvals.
Dataframe or matrix with variables in columns
Matrix or dataframe with p-values. The first column represents a
test across all 3 categories such as one-way ANOVA or likelihood ratio
test. Columns 2-4 represent pairwise tests comparing groups A vs B, A vs C
and B vs C, where A, B, C represent levels 1, 2, 3 in outcome. Columns
2-4 must be provided in the correct order. If pvals is not given, it is
calculated using the function calc_pvals.
Matrix or dataframe with adjusted p-values. If not supplied,
defaults to use nominal p-values from pvals.
Cut-off for p-value significance
Cut-off for fold change on radial axis
Vector of colours starting with non-significant variables
Optional character vector for labelling groups. Default NULL
leads to abbreviated labels based on levels in outcome using
abbreviate(). A vector of length 3 with custom abbreviated names for the
outcome levels can be supplied. Otherwise a vector length 7 is expected, of
the form "ns", "B+", "B+C+", "C+", "A+C+", "A+", "A+B+", where "ns" means
non-significant and A, B, C refer to levels 1, 2, 3 in outcome, and must
be in the correct order.
Optional arguments passed to calc_pvals
deseq_polar, voom_polar,
calc_pvals
data(example_data)
syn_polar <- polar_coords(outcome = syn_example_meta$Pathotype,
data = t(syn_example_rld))
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