DESpace can also be used to reveal the specific areas of the tissue affected by DSP genes; i.e., spatial clusters that are particularly over/under abundant compared to the average signal across conditions. This function can be used to identify SVGs among conditions for each individual cluster.
Usage
individual_dsp(
spe,
cluster_col,
sample_col,
condition_col,
min_counts = 20,
min_non_zero_spots = 10,
min_pct_cells = 0.5,
filter_gene = TRUE,
filter_cluster = TRUE
)Arguments
- spe
SpatialExperiment or SingleCellExperiment.
- cluster_col
Character. Column name of spatial clusters in
colData(spe).- sample_col
Character. Column name of sample ids in
colData(spe). Sample ids must be either a factor or character.- condition_col
Character. Column name of condition ids in
colData(spe).- min_counts
Numeric. Minimum number of counts per sample (across all spots) for a gene to be analyzed.
- min_non_zero_spots
Numeric. Minimum number of non-zero spots per sample, for a gene to be analyzed.
- min_pct_cells
Numeric. Minimum percentage of cells required for each cluster to be included in the analysis across the specified conditions. Default value is 0.5 (i.e., 0.5% of total cells per cluster per condition).
- filter_gene
Logical. If TRUE,
dsp_testfilters 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).- filter_cluster
Logical. When set to TRUE,
dsp_testexcludes clusters that are insufficiently represented in the dataset. Only clusters meeting the 'min_pct_cells' threshold (i.e., containing at least the specified percentage of cells across all conditions) will be retained for analysis.
Value
A list of results, with one result per spatial cluster in each element. Specifically, each item in the list is a "gene_results" dataframe which contains main edgeR test results.
Examples
# load the input data:
spe <- muSpaData::Wei22_example()
#> see ?muSpaData and browseVignettes('muSpaData') for documentation
#> loading from cache
set.seed(123)
results_individual_dsp <- individual_dsp(spe,
cluster_col = "Banksy_smooth",
sample_col = "sample_id",
condition_col = "condition")
#> Filter low quality genes:
#> min_counts = 20; min_non_zero_spots = 10.
#> The number of genes that pass filtering is 5000.
#> Conducting tests for layer '0' against all other layers.
#> Design model: row names represent sample names, followed by underscores and cluster names.
#> (Intercept) condition20DPI condition2DPI cluster_id0
#> 2DPI_1_Other 1 0 1 0
#> 2DPI_2_Other 1 0 1 0
#> condition20DPI:cluster_id0 condition2DPI:cluster_id0
#> 2DPI_1_Other 0 0
#> 2DPI_2_Other 0 0
#> Conducting tests for layer '1' against all other layers.
#> Design model: row names represent sample names, followed by underscores and cluster names.
#> (Intercept) condition20DPI condition2DPI cluster_id1
#> 2DPI_1_Other 1 0 1 0
#> 2DPI_2_Other 1 0 1 0
#> condition20DPI:cluster_id1 condition2DPI:cluster_id1
#> 2DPI_1_Other 0 0
#> 2DPI_2_Other 0 0
#> Conducting tests for layer '2' against all other layers.
#> Design model: row names represent sample names, followed by underscores and cluster names.
#> (Intercept) condition20DPI condition2DPI cluster_id2
#> 2DPI_1_Other 1 0 1 0
#> 2DPI_2_Other 1 0 1 0
#> condition20DPI:cluster_id2 condition2DPI:cluster_id2
#> 2DPI_1_Other 0 0
#> 2DPI_2_Other 0 0
#> Conducting tests for layer '3' against all other layers.
#> Design model: row names represent sample names, followed by underscores and cluster names.
#> (Intercept) condition20DPI condition2DPI cluster_id3
#> 2DPI_1_Other 1 0 1 0
#> 2DPI_2_Other 1 0 1 0
#> condition20DPI:cluster_id3 condition2DPI:cluster_id3
#> 2DPI_1_Other 0 0
#> 2DPI_2_Other 0 0
#> Conducting tests for layer '4' against all other layers.
#> Design model: row names represent sample names, followed by underscores and cluster names.
#> (Intercept) condition20DPI condition2DPI cluster_id4
#> 2DPI_1_Other 1 0 1 0
#> 2DPI_2_Other 1 0 1 0
#> condition20DPI:cluster_id4 condition2DPI:cluster_id4
#> 2DPI_1_Other 0 0
#> 2DPI_2_Other 0 0
#> Returning results
# We visualize results for the cluster '3'
results <- results_individual_dsp[['3']]
head(results,3)
#> gene_id logFC.condition20DPI.cluster_id3
#> AMEX60DD005921 AMEX60DD005921 -0.4165487
#> AMEX60DD002984 AMEX60DD002984 1.6438503
#> AMEX60DD009927 AMEX60DD009927 -0.8606185
#> logFC.condition2DPI.cluster_id3 logCPM LR PValue
#> AMEX60DD005921 1.724140 5.262614 38.17260 5.139552e-09
#> AMEX60DD002984 1.295172 7.473809 37.17236 8.474696e-09
#> AMEX60DD009927 -1.483164 8.534270 33.39838 5.592856e-08
#> FDR
#> AMEX60DD005921 2.118674e-05
#> AMEX60DD002984 2.118674e-05
#> AMEX60DD009927 9.321426e-05