Add a status column based on significance and estimated change
addDiffStatus(x, ...)
# S4 method for class '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 class 'DataFrame'
addDiffStatus(x, new_col = "status", ...)
# S4 method for class 'GRanges'
addDiffStatus(x, ...)
# S4 method for class 'GRangesList'
addDiffStatus(x, ...)
# S4 method for class 'SummarizedExperiment'
addDiffStatus(x, ...)Object to be classified
Used to pass arguments between methods
Name of the fold-change column
Name of the column with significance values
significance threshold
minimum estimated change to be considered in either of the up or down categories
factor levels to annotate regions based on the above criteria
Value to add when either fc_col or sig_col has NA values
name of the new column to be added
logical(1) Drop unused factor levels from the status column
An object of the same type as provided
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
## 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)
#> logFC p FDR status
#> 1 -0.3260365 0.060991105 0.08671830 Unchanged
#> 2 0.5524619 0.015486249 0.04602363 Increased
#> 3 -0.6749438 0.071312624 0.08671830 Unchanged
#> 4 0.2143595 0.014576927 0.04602363 Increased
#> 5 0.3107692 0.023011815 0.04602363 Increased
#> 6 1.1739663 0.057043745 0.08671830 Unchanged
#> 7 0.6187899 0.023005485 0.04602363 Increased
#> 8 -0.1127343 0.021889379 0.04602363 Decreased
#> 9 0.9170283 0.011252536 0.04602363 Increased
#> 10 -0.2232594 0.009209459 0.04602363 Decreased
#> 11 0.5264481 0.036822095 0.06137016 Unchanged
#> 12 -0.7948444 0.010761928 0.04602363 Decreased
#> 13 1.4277555 0.022449457 0.04602363 Increased
#> 14 -1.4668197 0.094882971 0.10542552 Unchanged
#> 15 -0.2366834 0.243430913 0.25624307 Unchanged
#> 16 -0.1933380 0.025494544 0.04635372 Decreased
#> 17 -0.8497547 0.019512443 0.04602363 Decreased
#> 18 0.0584655 0.073710555 0.08671830 Unchanged
#> 19 -0.8176704 0.259225245 0.25922524 Unchanged
#> 20 -2.0503078 0.072008584 0.08671830 Unchanged
## This works identically with a GRanges object, amongst others
gr <- GRanges(paste0("chr1:", seq_len(20)))
mcols(gr) <- df
addDiffStatus(gr)
#> GRanges object with 20 ranges and 4 metadata columns:
#> seqnames ranges strand | logFC p FDR status
#> <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric> <factor>
#> [1] chr1 1 * | -0.326036 0.0609911 0.0867183 Unchanged
#> [2] chr1 2 * | 0.552462 0.0154862 0.0460236 Increased
#> [3] chr1 3 * | -0.674944 0.0713126 0.0867183 Unchanged
#> [4] chr1 4 * | 0.214359 0.0145769 0.0460236 Increased
#> [5] chr1 5 * | 0.310769 0.0230118 0.0460236 Increased
#> ... ... ... ... . ... ... ... ...
#> [16] chr1 16 * | -0.1933380 0.0254945 0.0463537 Decreased
#> [17] chr1 17 * | -0.8497547 0.0195124 0.0460236 Decreased
#> [18] chr1 18 * | 0.0584655 0.0737106 0.0867183 Unchanged
#> [19] chr1 19 * | -0.8176704 0.2592252 0.2592252 Unchanged
#> [20] chr1 20 * | -2.0503078 0.0720086 0.0867183 Unchanged
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths