scde.posteriors: Calculate joint expression magnitude posteriors across a set...
In scde: Single Cell Differential Expression

Description Usage Arguments Value Examples

Calculates expression magnitude posteriors for the individual cells, and then uses bootstrap resampling to calculate a joint expression posterior for all the specified cells. Alternatively during batch-effect correction procedure, the joint posterior can be calculated for a random composition of cells of different groups (see batch and composition parameters).

scde.posteriors(models, counts, prior, n.randomizations = 100, batch = NULL,
  composition = NULL, return.individual.posteriors = FALSE,
  return.individual.posterior.modes = FALSE, ensemble.posterior = FALSE,
  n.cores = 20)

`models`	models models determined by `scde.error.models`
`counts`	read count matrix
`prior`	gene expression prior as determined by `scde.expression.prior`
`n.randomizations`	number of bootstrap iterations to perform
`batch`	a factor describing which batch group each cell (i.e. each row of `models` matrix) belongs to
`composition`	a vector describing the batch composition of a group to be sampled
`return.individual.posteriors`	whether expression posteriors of each cell should be returned
`return.individual.posterior.modes`	whether modes of expression posteriors of each cell should be returned
`ensemble.posterior`	Boolean of whether to calculate the ensemble posterior (sum of individual posteriors) instead of a joint (product) posterior. (default: FALSE)
`n.cores`	number of cores to utilize

default

a posterior probability matrix, with rows corresponding to genes, and columns to expression levels (as defined by prior$x)

return.individual.posterior.modes

a list is returned, with the $jp slot giving the joint posterior matrix, as described above. The $modes slot gives a matrix of individual expression posterior mode values on log scale (rows - genes, columns -cells)

return.individual.posteriors

a list is returned, with the $post slot giving a list of individual posterior matrices, in a form analogous to the joint posterior matrix, but reported on log scale

data(es.mef.small)
cd <- clean.counts(es.mef.small, min.lib.size=1000, min.reads = 1, min.detected = 1)
data(o.ifm)  # Load precomputed model. Use ?scde.error.models to see how o.ifm was generated
o.prior <- scde.expression.prior(models = o.ifm, counts = cd, length.out = 400, show.plot = FALSE)
# calculate joint posteriors
jp <- scde.posteriors(o.ifm, cd, o.prior, n.cores = 1)