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
)

Arguments

sce

A SingleCellExperiment-class object or one that inherits its properties.

var_registration

A character(1) specifying the colData(sce) variable of interest against which will be used for computing the relevant statistics.

var_sample_id

A character(1) specifying the colData(sce) variable with the sample ID.

covars

A character() with names of sample-level covariates.

min_ncells

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.

pseudobulk_rds_file

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.

Value

A pseudo-bulked SingleCellExperiment-class object.

See also

Examples

## 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)
#> 2024-07-26 23:49:06.229921 make pseudobulk object
#> 2024-07-26 23:49:06.412578 drop lowly expressed genes
#> 2024-07-26 23:49:06.467444 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