sort_tornado: Reorder features
In teunbrand/tornadoplot: Tornado Plot

Description Usage Arguments Value See Also Examples

The sort_tornado() function reorders the features of a TornadoExperiment object based on the weighted sum across all bins.

sort_tornado(
  tornado,
  assay_name = assayNames(tornado),
  bin_subset = NULL,
  bin_weights = NULL,
  sample_subset = NULL,
  decreasing = TRUE,
  ...
)

`tornado`	A TornadoExperiment object.
`assay_name`	A `character(1)`: one of the assay names.
`bin_subset, bin_weights`	Define either `bin_subset` or `bin_weights` (or neither), but not both. `bin_subset` should be a `logical` or `numeric` vector with subsetting indices for the number of bins. `bin_weights` should be a `numeric` vector of weights, the same length as there are bins. If both `bin_subset` and `bin_weights` are `NULL` (default), the weights are constructed with the density function of the Laplace distribution wherein the location is centred at the middle of the bins and the scale parameter is 10% of the number of bins.
`sample_subset`	A `character`, `logical` or `integer` vector for subsetting the samples.
`decreasing`	A `logical(1)`: sort from high to low (`TRUE`) or from low to high (`FALSE`)?
`...`	Not currently in use.

The tornado dataset with the features reordered.

Other tornado utilities: flatten_features(), melt_tornado(), norm_tornado()

# A tornado that isn't sorted
tor <- dummy_tornado()[c(2,1,4,3),]

# Sorting based on a particular sample
x <- sort_tornado(tor, sample_subset = "dummy_ctrl")

# Sorting based on a subset of bins
x <- sort_tornado(tor, bin_subset = 21:30)

# Sorting with custom weights
nbin <- nbin(tor)
w <- dnorm(seq_len(nbin), mean = (nbin + 1)/2, sd = 5)
x <- sort_tornado(tor, bin_weights = w)