runspatialdecon,Seurat-method {SpatialDecon} | R Documentation |
A wrapper for applying spatialdecon to the Spatial data element in a Seurat object. Unlike spatialdecon, which expects a normalized data matrix, this function operates on raw counts. Scaling for total cells
## S4 method for signature 'Seurat' runspatialdecon( object, X = NULL, bg = 0.1, 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 seurat object. Must include a "Spatial" element in the "assays" slot. |
X |
Cell profile matrix. If NULL, the safeTME matrix is used. |
bg |
Expected background counts. Either a scalar applied equally to all points in the count matrix, or a matrix with the same dimensions as the count matrix in GetAssayData(object, assay = "Spatial"). Recommended to use a small non-zero value, default of 0.1. |
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 list including the following items:
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
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))).
X: the cell profile matrix used in the decon fit.
sigmas: covariance matrices of each observation's beta estimates
if given cellmerges, the list will additionally include the following items
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 list will additionally include the following items
cell.counts: beta rescaled to estimate cell numbers, based on prop_of_all and nuclei count
if given both cellmerges and cell_counts, the list will additionally include the following items
cell.counts.granular: cell.counts prior to combining closely-related cell types
# get dataset con <- gzcon(url("https://github.com/almaan/her2st/raw/master/data/ST-cnts/G1.tsv.gz")) txt <- readLines(con) temp <- read.table(textConnection(txt), sep = "\t", header = TRUE, row.names = 1) # parse data raw = t(as.matrix(temp)) norm = sweep(raw, 2, colSums(raw), "/") * mean(colSums(raw)) x = as.numeric(substr(rownames(temp), 1, unlist(gregexpr("x", rownames(temp))) - 1)) y = -as.numeric(substr(rownames(temp), unlist(gregexpr("x", rownames(temp))) + 1, nchar(rownames(temp)))) # put into a seurat object: andersson_g1 = SeuratObject::CreateSeuratObject(counts = raw, assay="Spatial") andersson_g1@meta.data$x = x andersson_g1@meta.data$y = y res <- runspatialdecon(andersson_g1) str(res)