runspatialdecon,NanoStringGeoMxSet-method {SpatialDecon} | R Documentation |
A wrapper for applying spatialdecon to a NanostringGeomxSet object.
## S4 method for signature 'NanoStringGeoMxSet' runspatialdecon( object, X = NULL, norm_elt = NULL, raw_elt = NULL, wts = NULL, resid_thresh = 3, lower_thresh = 0.5, align_genes = TRUE, is_pure_tumor = NULL, n_tumor_clusters = 10, cell_counts = NULL, cellmerges = NULL, maxit = 1000 )
object |
A NanostringGeomxSet object. |
X |
Cell profile matrix. If NULL, the safeTME matrix is used. |
norm_elt |
normalized data element in assayData |
raw_elt |
raw data element in assayData |
wts |
Optional, a matrix of weights. |
resid_thresh |
A scalar, sets a threshold on how extreme individual data points' values can be (in log2 units) before getting flagged as outliers and set to NA. |
lower_thresh |
A scalar. Before log2-scale residuals are calculated, both observed and fitted values get thresholded up to this value. Prevents log2-scale residuals from becoming extreme in points near zero. |
align_genes |
Logical. If TRUE, then Y, X, bg, and wts are row-aligned by shared genes. |
is_pure_tumor |
A logical vector denoting whether each AOI consists of pure tumor. If specified, then the algorithm will derive a tumor expression profile and merge it with the immune profiles matrix. |
n_tumor_clusters |
Number of tumor-specific columns to merge into the cell profile matrix. Has an impact only when is_pure_tumor argument is used to indicate pure tumor AOIs. Takes this many clusters from the pure-tumor AOI data and gets the average expression profile in each cluster. Default 10. |
cell_counts |
Number of cells estimated to be within each sample. If provided alongside norm_factors, then the algorithm will additionally output cell abundance esimtates on the scale of cell counts. |
cellmerges |
A list object holding the mapping from beta's cell names to combined cell names. If left NULL, then defaults to a mapping of granular immune cell definitions to broader categories. |
maxit |
Maximum number of iterations. Default 1000. |
if not given cellmerges and cell_counts, a valid GeoMx S4 object including the following items
In pData
beta: matrix of cell abundance estimates, cells in rows and observations in columns
p: matrix of p-values for H0: beta == 0
t: matrix of t-statistics for H0: beta == 0
se: matrix of standard errors of beta values
prop_of_all: rescaling of beta to sum to 1 in each observation
prop_of_nontumor: rescaling of beta to sum to 1 in each observation, excluding tumor abundance estimates
sigmas: covariance matrices of each observation's beta estimates
In assayData
yhat: a matrix of fitted values
resids: a matrix of residuals from the model fit. (log2(pmax(y, lower_thresh)) - log2(pmax(xb, lower_thresh))).
In experimentData
SpatialDeconMatrix: the cell profile matrix used in the decon fit.
if given cellmerges, the valid GeoMx S4 object will additionally include the following items
In pData
beta.granular: cell abundances prior to combining closely-related cell types
sigma.granular: sigmas prior to combining closely-related cell types
if given cell_counts, the valid GeoMx S4 object will additionally include the following items
In pData
cell.counts: beta rescaled to estimate cell numbers, based on prop_of_all and nuclei count
if given both cellmerges and cell_counts, the valid GeoMx S4 object will additionally include the following items
In pData
cell.counts.granular: cell.counts prior to combining closely-related cell types
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") demoData <- runspatialdecon(object = target_demoData, norm_elt = "exprs_norm", raw_elt = "exprs")