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Variance decomposition

Source: R/AllGenerics.R, R/paceFit.R
paceDecompose.Rd

Partitions per-gene expression variance into cell-type identity, spatial cell state, contamination, and residual (plus a responder block for condition cohorts), populating varianceDecomposition(). Needs the fitted object plus the same SpatialExperiment used for paceModel() (to read the counts).

Usage

paceDecompose(object, ...)

# S4 method for class 'PACEFit'
paceDecompose(object, spe, ...)

Arguments

object

A PACEFit from paceModel().

...

Unused.

spe

The SpatialExperiment::SpatialExperiment that was fitted.

Value

The PACEFit with the variance decomposition added.

Details

The decomposition needs per-cell-type statistics of the fitted means, which the fit stores; it never builds a cells x genes matrix. An older fit without those statistics has them computed from its stored fitted means, or, if those were dropped, rebuilt exactly from the fit and spe a block of genes at a time.

Examples

spe <- readRDS(system.file("extdata", "bc_xenium_subset.rds", package = "PACE"))
fit <- readRDS(system.file("extdata", "pace_fit_example.rds", package = "PACE"))
fit <- paceDecompose(fit, spe)
head(varianceDecomposition(fit))
#>          focal    gene Cell type %    Spatial % Spillover % Residual %
#> SEC11C  B_Cell  SEC11C  18.7147181 14.494678932   0.6373641   66.15324
#> DAPK3   B_Cell   DAPK3  14.5989691  0.002760858   1.0526045   84.34567
#> TCIM    B_Cell    TCIM  85.9791365  0.002251981   0.5945541   13.42406
#> NKG7    B_Cell    NKG7   0.3900736  0.002239134   8.2668800   91.34081
#> RAPGEF3 B_Cell RAPGEF3  14.2060602  0.008126525   3.2152159   82.57060
#> PPARG   B_Cell   PPARG   9.2327061  0.013567627   4.4774151   86.27631
#>           SS_lineage  SS_within      denom n_focal
#> SEC11C   638.5745510 2773.57704 3412.15159     330
#> DAPK3     14.4759924   84.68164   99.15763     330
#> TCIM    2153.2706156  351.13999 2504.41061     330
#> NKG7       0.3704028   94.58676   94.95716     330
#> RAPGEF3   37.1808640  224.54451  261.72537     330
#> PPARG     31.1183156  305.92605  337.04436     330