runspatialdecon,Seurat-method {SpatialDecon}R Documentation

Run spatialdecon on a Seurat object

Description

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

Usage

## 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
)

Arguments

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.

Value

if not given cellmerges and cell_counts, a list including the following items:

if given cellmerges, the list will additionally include the following items

if given cell_counts, the list will additionally include the following items

if given both cellmerges and cell_counts, the list will additionally include the following items

Examples

# 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)

[Package SpatialDecon version 1.4.3 Index]