reWeightEmbedding: Re-weight StabMap embedding

In MarioniLab/StabMap: Stabilised mosaic single cell data integration using unshared features

Re-weight StabMap embedding

Description

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)

# 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)

# 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))

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

MarioniLab/StabMap documentation built on Sept. 28, 2022, 2:28 a.m.