Spatial registration: model

This function defines the statistical model that will be used for computing the block correlation as well as pairwise statistics. It is useful to check it in case your sample-level covariates need to be casted. For example, an integer() variable might have to be casted into a factor() if you wish to model it as a categorical variable and not a continuous one.

registration_model(
  sce_pseudo,
  covars = NULL,
  var_registration = "registration_variable"
)

Arguments

sce_pseudo

The output of registration_pseudobulk().

covars

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

var_registration

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

Value

The output of model.matrix() which you can inspect to verify that your sample-level covariates are being properly modeled.

See also

Other spatial registration and statistical modeling functions: registration_block_cor(), registration_pseudobulk(), registration_stats_anova(), registration_stats_enrichment(), registration_stats_pairwise(), registration_wrapper()

Examples

example("registration_pseudobulk", package = "spatialLIBD")
#> 
#> rgstr_> ## Ensure reproducibility of example data
#> rgstr_> set.seed(20220907)
#> 
#> rgstr_> ## Generate example data
#> rgstr_> sce <- scuttle::mockSCE()
#> 
#> rgstr_> ## Add some sample IDs
#> rgstr_> sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE)
#> 
#> rgstr_> ## Add a sample-level covariate: age
#> rgstr_> ages <- rnorm(5, mean = 20, sd = 4)
#> 
#> rgstr_> names(ages) <- LETTERS[1:5]
#> 
#> rgstr_> sce$age <- ages[sce$sample_id]
#> 
#> rgstr_> ## Add gene-level information
#> rgstr_> rowData(sce)$ensembl <- paste0("ENSG", seq_len(nrow(sce)))
#> 
#> rgstr_> rowData(sce)$gene_name <- paste0("gene", seq_len(nrow(sce)))
#> 
#> rgstr_> ## Pseudo-bulk
#> rgstr_> sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL)
#> 2024-05-09 15:11:12.734682 make pseudobulk object
#> 2024-05-09 15:11:12.92834 drop lowly expressed genes
#> 2024-05-09 15:11:12.985913 normalize expression
#> 
#> rgstr_> 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
registration_mod <- registration_model(sce_pseudo, "age")
#> 2024-05-09 15:11:13.062282 create model matrix
head(registration_mod)
#>      registration_variableG0 registration_variableG1 registration_variableG2M
#> A_G0                       1                       0                        0
#> B_G0                       1                       0                        0
#> C_G0                       1                       0                        0
#> D_G0                       1                       0                        0
#> E_G0                       1                       0                        0
#> A_G1                       0                       1                        0
#>      registration_variableS      age
#> A_G0                      0 19.18719
#> B_G0                      0 25.34965
#> C_G0                      0 24.18019
#> D_G0                      0 15.52107
#> E_G0                      0 20.97006
#> A_G1                      0 19.18719