Impute values using StabMap joint embedding

Performs naive imputation of values from the list of mosaic data and joint embedding from StabMap.

Usage

imputeEmbedding(
  assay_list,
  embedding,
  reference = Reduce(union, lapply(assay_list, colnames)),
  query = Reduce(union, lapply(assay_list, colnames)),
  neighbours = 5,
  fun = mean
)

Arguments

assay_list

List of mosaic data from which to perform imputation.

embedding

Joint embedding from which to extract nearest neighbour relationships.

reference

Character vector of cell names to treat as reference cells.

query

Character vector of cell names to treat as query cells.

neighbours

Number of nearest neighbours to consider (default 5).

fun

function (default `mean`) to aggregate nearest neighbours' imputed values.

Value

List containing imputed values from each assay_list data matrix which contains reference cells.

Examples

set.seed(2021)
assay_list <- mockMosaicData()
lapply(assay_list, dim)
#> $D1
#> [1] 150  50
#> 
#> $D2
#> [1] 150  50
#> 
#> $D3
#> [1] 150  50
#> 

# stabMap
out <- stabMap(assay_list,
  ncomponentsReference = 20,
  ncomponentsSubset = 20
)

#> treating "D1" as reference
#> generating embedding for path with reference "D1": "D1"
#> generating embedding for path with reference "D1": "D2" -> "D1"
#> generating embedding for path with reference "D1": "D3" -> "D2" -> "D1"
#> treating "D2" as reference
#> generating embedding for path with reference "D2": "D2"
#> generating embedding for path with reference "D2": "D1" -> "D2"
#> generating embedding for path with reference "D2": "D3" -> "D2"
#> treating "D3" as reference
#> generating embedding for path with reference "D3": "D3"
#> generating embedding for path with reference "D3": "D2" -> "D3"
#> generating embedding for path with reference "D3": "D1" -> "D2" -> "D3"

# impute values
imp <- imputeEmbedding(assay_list, out)

# inspect the imputed values
lapply(imp, dim)
#> $D1
#> [1] 150 150
#> 
#> $D2
#> [1] 150 150
#> 
#> $D3
#> [1] 150 150
#> 
imp[[1]][1:5, 1:5]
#>          D1_cell_1   D1_cell_2  D1_cell_3   D1_cell_4   D1_cell_5
#> gene_1  0.50687450 -0.77149007 -0.2982274 -0.72078317 -0.12148316
#> gene_2  0.05829149 -0.05286518 -0.1375450  0.02383978 -0.35894336
#> gene_3  1.21645830  0.05262619  0.7272864  0.18519759  0.60755035
#> gene_4 -0.49139466  0.23012490  0.1569971 -0.12349555 -0.00984099
#> gene_5 -0.04233858 -0.33526072 -0.2985623 -0.43112877 -0.13828083