spatialdecon {SpatialDecon} | R Documentation |
Runs the spatialdecon algorithm with added optional functionalities. Workflow is:
compute weights from raw data
Estimate a tumor profile and merge it into the cell profiles matrix
run deconvolution once
remove poorly-fit genes from first round of decon
re-run decon with cleaned-up gene set
combine closely-related cell types
compute p-values
rescale abundance estimates, to proportions of total, proportions of immune, cell counts
spatialdecon( norm, bg, X = NULL, raw = 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 )
norm |
p-length expression vector or p * N expression matrix - the actual (linear-scale) data |
bg |
Same dimension as norm: the background expected at each data point. |
X |
Cell profile matrix. If NULL, the safeTME matrix is used. |
raw |
Optional for using an error model to weight the data points. p-length expression vector or p * N expression matrix - the raw (linear-scale) data |
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. |
a list:
beta: matrix of cell abundance estimates, cells in rows and observations in columns
sigmas: covariance matrices of each observation's beta estimates
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
cell.counts: beta rescaled to estimate cell numbers, based on prop_of_all and nuclei count
beta.granular: cell abundances prior to combining closely-related cell types
sigma.granular: sigmas prior to combining closely-related cell types
cell.counts.granular: cell.counts prior to combining closely-related cell types
resids: a matrix of residuals from the model fit. (log2(pmax(y, lower_thresh)) - log2(pmax(xb, lower_thresh))).
X: the cell profile matrix used in the decon fit.
data(mini_geomx_dataset) data(safeTME) data(safeTME.matches) # estimate background: mini_geomx_dataset$bg <- derive_GeoMx_background( norm = mini_geomx_dataset$normalized, probepool = rep(1, nrow(mini_geomx_dataset$normalized)), negnames = "NegProbe" ) # run basic decon: res0 <- spatialdecon( norm = mini_geomx_dataset$normalized, bg = mini_geomx_dataset$bg, X = safeTME ) # run decon with bells and whistles: res <- spatialdecon( norm = mini_geomx_dataset$normalized, bg = mini_geomx_dataset$bg, X = safeTME, cellmerges = safeTME.matches, cell_counts = mini_geomx_dataset$annot$nuclei, is_pure_tumor = mini_geomx_dataset$annot$AOI.name == "Tumor" )