svg_test

Usage

svg_test(
  spe,
  cluster_col,
  sample_col = NULL,
  replicates = FALSE,
  min_counts = 20,
  min_non_zero_spots = 10,
  filter_gene = TRUE,
  verbose = FALSE
)

Arguments

spe

SpatialExperiment or SingleCellExperiment.

cluster_col

Column name of spatial clusters in colData(spe).

sample_col

Column name of sample ids in colData(spe).

replicates

A logical, indicating whether biological replicates are provided (TRUE) or not (FALSE). If biological replicates are provided, svg_test performs a joint test across all replicates, searching for SVGs with consistent spatial patterns across samples.

min_counts

Minimum number of counts per sample (across all spots) for a gene to be analyzed.

min_non_zero_spots

Minimum number of non-zero spots per sample, for a gene to be analyzed.

filter_gene

A logical. If TRUE, svg_test filters genes: genes have to be expressed in at least 'min_non_zero_spots' spots, and a gene requires at least 'min counts' counts per sample (across all locations).

verbose

A logical. If TRUE, svg_test returns two more results: 'DGEGLM' and 'DGELRT' objects contain full statistics from 'edgeR::glmFit' and 'edgeR::glmLRT'.

Value

A list of results:

- "gene_results": a dataframe contains main edgeR test results;

- "estimated_y": a DGEList object contains the estimated common dispersion, which can later be used to speed-up calculation when testing individual clusters.

- "glmFit" (only if verbose = TRUE): a DGEGLM object contains full statistics from "edgeR::glmFit".

- "glmLRT" (only if verbose = TRUE): a DGELRT object contains full statistics from "edgeR::glmLRT".

Details

If 'sample_col' is not specified and 'replicates == FALSE', svg_test assumed that data comes from an individual sample, and performs SV testing on it.

If 'sample_col' is provided and 'replicates == FALSE', svg_test tests each sample individually and returns a list of results for each sample.

If 'sample_col' is provided and 'replicates == TRUE', svg_test performs a joint multi-sample test.

See also

Examples

# load the input data:
data("LIBD_subset", package = "DESpace")
LIBD_subset
#> class: SpatialExperiment 
#> dim: 500 3639 
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(500): ENSG00000282097 ENSG00000225129 ... ENSG00000267199
#>   ENSG00000273129
#> rowData names(0):
#> colnames(3639): AAACAAGTATCTCCCA-1 AAACAATCTACTAGCA-1 ...
#>   TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(4): array_row array_col layer_guess_reordered sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(0) :
#> imgData names(0):

# Fit the model via \code{\link{svg_test}} function.
set.seed(123)
results_svg_test <- svg_test(spe = LIBD_subset,
                             cluster_col = "layer_guess_reordered",
                             verbose = FALSE)
#> using 'svg_test' for spatial gene/pattern detection.
#> Filter low quality genes: 
#> min_counts = 20; min_non_zero_spots = 10.
#> The number of genes that pass filtering is 237.
#> single sample test

# svg_test returns of a list of 2 objects:
# "gene_results": a dataframe contains main edgeR test results;
# "estimated_y": a DGEList object contains the estimated common dispersion, 
#  which can later be used to speed-up calculation when testing individual clusters.

# We visualize differential results:
head(results_svg_test$gene_results, 3)
#>                         gene_id        LR   logCPM        PValue           FDR
#> ENSG00000115756 ENSG00000115756 1064.0595 15.89723 1.244217e-226 2.948795e-224
#> ENSG00000054690 ENSG00000054690 1053.5186 15.48088 2.372435e-224 2.811335e-222
#> ENSG00000111716 ENSG00000111716  752.3446 16.58993 3.037426e-159 2.399567e-157