Fit the PACE model to continuous intensities

The Gaussian, identity-link form of paceModel(), for continuous measurements such as imaging mass cytometry protein intensities rather than counts.

Usage

paceModelGaussian(object, ...)

# S4 method for class 'SpatialExperiment'
paceModelGaussian(
  object,
  celltype_col,
  image_col = NULL,
  condition_col = NULL,
  assay_name = "intensity",
  h_bio = 30,
  h_tech = 5,
  kernel_per_image = FALSE,
  image_re = c("none", "intercept", "slopes", "condition_slopes"),
  types = NULL,
  resp_term = NULL,
  verbose = TRUE,
  ...
)

Arguments

object

A SpatialExperiment::SpatialExperiment.

...

Passed to pace_fit_streaming_gaussian().

celltype_col

Column of colData holding the cell type labels.

image_col

Column of colData identifying the image. Defaults to the single image when there is only one.

condition_col

Optional column of colData giving a condition contrast.

assay_name

The assay holding the intensities.

h_bio, h_tech

Bandwidths of the biological and technical kernels.

kernel_per_image

Build the kernels within each image separately.

image_re

An optional second random-effect block over images.

types

Cell types, in the order the fit should use them.

resp_term

Optional response term passed to the model frame.

verbose

Print the fitting trace.

Value

A PACEFit.

Details

The working response on the identity link is the intensity itself and the working weight is the per-gene residual variance, so the mean converges in a single inner solve and the outer iteration only moves the variance components.

This path does not model technical contamination. Correct the intensities first, against an ambient field built with ambientField(), and pass the corrected matrix here.