Pseudo-bulk the gene expression, filter lowly-expressed genes, and normalize. This is the first step for spatial registration and for statistical modeling.
registration_pseudobulk(
sce,
var_registration,
var_sample_id,
covars = NULL,
min_ncells = 10,
pseudobulk_rds_file = NULL
)A SingleCellExperiment-class object or one that inherits its properties.
A character(1) specifying the colData(sce)
variable of interest against which will be used for computing the relevant
statistics.
A character(1) specifying the colData(sce) variable
with the sample ID.
A character() with names of sample-level covariates.
An integer(1) greater than 0 specifying the minimum
number of cells (for scRNA-seq) or spots (for spatial) that are combined
when pseudo-bulking. Pseudo-bulked samples with less than min_ncells on
sce_pseudo$ncells will be dropped.
A character(1) specifying the path for saving
an RDS file with the pseudo-bulked object. It's useful to specify this since
pseudo-bulking can take hours to run on large datasets.
A pseudo-bulked SingleCellExperiment-class object.
Other spatial registration and statistical modeling functions:
registration_block_cor(),
registration_model(),
registration_stats_anova(),
registration_stats_enrichment(),
registration_stats_pairwise(),
registration_wrapper()
## Ensure reproducibility of example data
set.seed(20220907)
## Generate example data
sce <- scuttle::mockSCE()
## Add some sample IDs
sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE)
## Add a sample-level covariate: age
ages <- rnorm(5, mean = 20, sd = 4)
names(ages) <- LETTERS[1:5]
sce$age <- ages[sce$sample_id]
## Add gene-level information
rowData(sce)$ensembl <- paste0("ENSG", seq_len(nrow(sce)))
rowData(sce)$gene_name <- paste0("gene", seq_len(nrow(sce)))
## Pseudo-bulk
sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL)
#> 2024-04-19 19:44:18.548976 make pseudobulk object
#> 2024-04-19 19:44:18.710654 drop lowly expressed genes
#> 2024-04-19 19:44:18.766914 normalize expression
colData(sce_pseudo)
#> DataFrame with 20 rows and 8 columns
#> Mutation_Status Cell_Cycle Treatment sample_id age
#> <character> <character> <character> <character> <numeric>
#> A_G0 NA G0 NA A 19.1872
#> B_G0 NA G0 NA B 25.3496
#> C_G0 NA G0 NA C 24.1802
#> D_G0 NA G0 NA D 15.5211
#> E_G0 NA G0 NA E 20.9701
#> ... ... ... ... ... ...
#> A_S NA S NA A 19.1872
#> B_S NA S NA B 25.3496
#> C_S NA S NA C 24.1802
#> D_S NA S NA D 15.5211
#> E_S NA S NA E 20.9701
#> registration_variable registration_sample_id ncells
#> <character> <character> <integer>
#> A_G0 G0 A 8
#> B_G0 G0 B 13
#> C_G0 G0 C 9
#> D_G0 G0 D 7
#> E_G0 G0 E 10
#> ... ... ... ...
#> A_S S A 12
#> B_S S B 8
#> C_S S C 7
#> D_S S D 14
#> E_S S E 11