runReverseDecon,NanoStringGeoMxSet-method {SpatialDecon} | R Documentation |
A wrapper for applying reversedecon to a NanostringGeomxSet object.
## S4 method for signature 'NanoStringGeoMxSet' runReverseDecon(object, norm_elt = NULL, beta, epsilon = NULL)
object |
A NanostringGeomxSet object. |
norm_elt |
normalized data element in assayData. |
beta |
Matrix of cell abundance estimates, with cells in columns and observations in rows. Columns are aligned to "norm". |
epsilon |
All y and yhat values are thresholded up to this point when performing decon. Essentially says, "ignore variability in counts below this threshold." |
a valid GeoMx S4 object including the following items:
in fData
coefs, a matrix of coefficients for genes * cells, where element i,j is interpreted as "every unit increase in cell score j is expected to increase expression of gene i by _".
cors, a vector giving each gene's correlation between fitted and observed expression
resid.sd, a vector of each gene's residual SD, a metric of how much variability genes have independend of cell mixing.
in assayData
yhat, a matrix of fitted values, in the same dimension as norm
resids, a matrix of log2-scale residuals from the reverse decon fit, in the same dimension as norm
library(GeomxTools) datadir <- system.file("extdata", "DSP_NGS_Example_Data", package = "GeomxTools") demoData <- readRDS(file.path(datadir, "/demoData.rds")) demoData <- shiftCountsOne(demoData) target_demoData <- aggregateCounts(demoData) target_demoData <- normalize(target_demoData, "quant") # run basic decon: res0 <- runspatialdecon(object = target_demoData, norm_elt = "exprs_norm", raw_elt = "exprs") # run reverse decon: target_demoData <- runReverseDecon(object = target_demoData, norm_elt = "exprs_norm", beta = pData(res0)$beta)