applySpline: applies a spline smoothing to a tidy data.table containing x...

In jrboyd/seqsetvis: Set Based Visualizations for Next-Gen Sequencing Data

applies a spline smoothing to a tidy data.table containing x and y values.

Description

applySpline Is intended for two-dimensional tidy data.tables, as retured by ssvFetchBigwig

Usage

applySpline(
  dt,
  n,
  x_ = "x",
  y_ = "y",
  by_ = c("id", "sample"),
  splineFun = stats::spline
)

Arguments

dt	a tidy data.table containing two-dimensional data
n	the number of interpolation points to use per input point, see ?spline. n must be > 1.
x_	the variable name of the x-values
y_	the variable name of the y-values
by_	optionally, any variables that provide grouping to the data. default is none. see details.
splineFun	a function that accepts x, y, and n as arguments and returns a list of length 2 with named elements x and y. stats::spline by default. see stats::spline for details.

Details

by_ is quite powerful. If by_ = c('gene_id', 'sample_id'), splines will be calculated individually for each gene in each sample. alternatively if by_ = c('gene_id')

Value

a newly derived data.table that is n times longer than original.

See Also

ssvFetchBigwig

Examples

#data may be blockier than we'd like
ggplot(CTCF_in_10a_profiles_dt[, list(y = mean(y)), by = list(sample, x)]) +
    geom_line(aes(x = x, y = y, color = sample))

#can be smoothed by applying a spline  (think twice about doing so,
#it may look prettier but may also be deceptive or misleading)

splined_smooth = applySpline(CTCF_in_10a_profiles_dt, n = 10,
    y_ = 'y', by_ = c('id', 'sample'))
ggplot(splined_smooth[, list(y = mean(y)), by = list(sample, x)]) +
    geom_line(aes(x = x, y = y, color = sample))

jrboyd/seqsetvis documentation built on March 17, 2024, 3:14 p.m.