Re-weight StabMap embedding

Re-weights embedding according to given weights for each reference dataset. This gives more or less weighting to each contributing dataset and method (PCA or LDA),

Usage

reWeightEmbedding(embedding, weights = NULL, factor = 1e+06)

Arguments

embedding

Joint embedding as output from stabMap.

weights

(optional) named numeric vector giving relative weights for each reference dataset.

factor

numeric multiplicative value to offset near-zero values.

Value

matrix of same dimensions as `embedding`.

Examples

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

# specify which datasets to use as reference coordinates
reference_list <- c("D1", "D3")

# specify some sample labels to distinguish using linear discriminant
# analysis (LDA)
labels_list <- list(
  D1 = rep(letters[1:5], length.out = ncol(assay_list[["D1"]]))
)

# stabMap
out <- stabMap(assay_list,
  reference_list = reference_list,
  labels_list = labels_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"
#> labels provided for "D1", adding LD components
#> 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 "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"

# look at the scale of each component and discriminant
boxplot(out, las = 2, outline = FALSE)


# re-weight embedding for less contribution from LDs and equal contribution
# from PCs of both references
out_reweighted <- reWeightEmbedding(
  out,
  weights = c("D1_LD" = 0.5, "D1_PC" = 1, "D3_PC" = 1)
)
#> reweighting for references: D1_LDD1_PCD3_PC

# look at the new scale of each component and discriminant
boxplot(out_reweighted, las = 2, outline = FALSE)