addDiffStatus | R Documentation |
Add a status column based on significance and estimated change
addDiffStatus(x, ...)
## S4 method for signature 'data.frame'
addDiffStatus(
x,
fc_col = "logFC",
sig_col = c("FDR", "hmp_fdr", "p_fdr", "adj.P.Value"),
alpha = 0.05,
cutoff = 0,
up = "Increased",
down = "Decreased",
other = "Unchanged",
missing = "Undetected",
new_col = "status",
drop = FALSE,
...
)
## S4 method for signature 'DataFrame'
addDiffStatus(x, new_col = "status", ...)
## S4 method for signature 'GRanges'
addDiffStatus(x, ...)
## S4 method for signature 'GRangesList'
addDiffStatus(x, ...)
## S4 method for signature 'SummarizedExperiment'
addDiffStatus(x, ...)
x |
Object to be classified |
... |
Used to pass arguments between methods |
fc_col |
Name of the fold-change column |
sig_col |
Name of the column with significance values |
alpha |
significance threshold |
cutoff |
minimum estimated change to be considered in either of the up or down categories |
up , down , other |
factor levels to annotate regions based on the above criteria |
missing |
Value to add when either fc_col or sig_col has NA values |
new_col |
name of the new column to be added |
drop |
logical(1) Drop unused factor levels from the status column |
This takes a simple object and adds a new column classifying entries into
one of three categories, as specified using up
, down
or other
.
Results in the new column will always be returned as a factor with levels in
order of the values provided in the arguments other
, down
and up
An object of the same type as provided
## Working with a data.frame
set.seed(101)
df <- data.frame(logFC = rnorm(20), p = rbeta(20, shape1 = 1, shape2 = 20))
df$FDR <- p.adjust(df$p, "fdr")
addDiffStatus(df)
## This works identically with a GRanges object, amongst others
gr <- GRanges(paste0("chr1:", seq_len(20)))
mcols(gr) <- df
addDiffStatus(gr)
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