STdiff: STdiff: Differential gene expression analysis for spatial...

In FridleyLab/spatialGE: Visualization and Analysis of Spatial Heterogeneity in Spatially-Resolved Gene Expression

STdiff: Differential gene expression analysis for spatial transcriptomics data

Description

Tests for differentially expressed genes using linear models with or without spatial covariance structures

Usage

STdiff(
  x = NULL,
  samples = NULL,
  annot = NULL,
  w = NULL,
  k = NULL,
  deepSplit = NULL,
  topgenes = 5000,
  pval_thr = 0.05,
  pval_adj = "fdr",
  test_type = "mm",
  sp_topgenes = 0.2,
  clusters = NULL,
  pairwise = F,
  verbose = 1L,
  cores = NULL
)

Arguments

x	an STlist
samples	an integer indicating the spatial samples to be included in the DE tests. Numbers follow the order in names(x@counts). Sample names are also allowed. If NULL, performs tests on all samples
annot	a column name in x@spatial_meta containing the groups/clusters to be tested. Required if k and w are empty.
w	the spatial weight used in STclust. Required if annot is empty.
k	the k value used in STclust, or dtc for dynamicTreeCut clusters. Required if annot is empty.
deepSplit	the deepSplit value if used in STclust. Required if k='dtc'.
topgenes	an integer indicating the top variable genes to select from each sample based on variance (default=5000). If NULL, all genes are selected.
pval_thr	cut-off of adjusted p-values to define differentially expressed genes from non-spatial linear models. A proportion of genes (sp_topgenes) under this cut-off will be applied the spatial models. Default=0.05
pval_adj	Method to adjust p-values. Defaults to FDR. Other options as available from p.adjust
test_type	one of mm, t_test, or wilcoxon. Specifies the type of test performed.
sp_topgenes	Proportion of differentially expressed genes from non-spatial linear models (and controlled by pval_thr) to use in differential gene expression analysis with spatial linear models. If 0 (zero), no spatial models are fit. Default=0.2
clusters	cluster name(s) to test DE genes, as opposed to all clusters.
pairwise	whether or not to carry tests on a pairwise manner. The default is pairwise=F, meaning that DE genes are tested by comparing each cluster to the rest of the pooled cell/spots.
verbose	output progress indicators. If verbose=0, no text is shown in console. Other values are 1 and 2 indicating increasing level of verbosity. Default is verbose=1
cores	Number of cores to use in parallelization. If NULL, the number of cores to use is detected automatically

Details

The method tests for differentially expressed genes between groups of spots/cells (e.g., clusters) in a spatial transcriptomics sample. Specifically, the function tests for genes with significantly higher or lower gene expression in one group of spots/cells with respect to the rest of spots/cells in the sample. The method first runs non-spatial linear models on the genes to detect differentially expressed genes. Then spatial linear models with exponential covariance structure are fit on a subset of genes detected as differentially expressed by the non-linear models (sp_topgenes). If running on clusters detected via STclust, the user can specify the assignments using the same parameters (w, k, deepSplit). Otherwise, the assignments are specified by indicating one of the column names in x@spatial_meta. The function uses spaMM::fitme and is computationally expensive even on HPC environments. To run the STdiff using the non-spatial approach (faster), set sp_topgenes=0.

Value

a list with one data frame per sample with results of differential gene expression analysis

FridleyLab/spatialGE documentation built on April 14, 2025, 9:37 a.m.