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Iteratively reweighted least squares (IRLS) in scMultiMap

scMultiMap_IRLS.Rd

This function implements the 1st step of scMultiMap: estimate mean and variance for peaks or genes with IRLS. The output will be used as input to the 2nd step, scMultiMap_WLS(), to estimate and infer the association between genes and peaks.

Usage

scMultiMap_IRLS(X, seq_depth, bsample = NULL, irls = T, verbose = F)

Source

Cell-type-specific mapping of enhancer and target genes from single-cell multimodal data. Chang Su, Dongsoo Lee, Peng Jin, Jingfei Zhang; https://www.biorxiv.org/content/10.1101/2024.09.24.614814v1

Arguments

X

A n by p count matrix, where n denotes the number of cells and p denotes the number of genes/peaks. It can be a length p vector when n=1.

seq_depth

A length n vector of sequencing depths

bsample

A length n vector of the names of biological sample the cell is from. When all cells are from the same sample, set to NULL (Default).

irls

Whether to use IRLS. Default to TRUE. If FALSE, use ordinary least squares.

verbose

Whether to print detailed messages. Default to FALSE.

Value

A list of two length p vectors:

mu

estimated mean

sigma_sq

estimated variance

Examples

# The following codes illustrate the use of `scMultiMap_IRLS` with toy datasets
# `small_obj` provided in this R package.
irls_list <- list()
irls_list[['gene']] <- scMultiMap_IRLS(t(as.matrix(small_obj[['RNA']]$counts)),
 Matrix::colSums(small_obj[['RNA']]$counts))
irls_list[['peak']] <- scMultiMap_IRLS(t(as.matrix(small_obj[['peak']]$counts)),
 Matrix::colSums(small_obj[['peak']]$counts))
print(str(irls_list[['gene']])) # estimated mean and variance for genes
#> List of 3
#>  $ mu      : Named num [1:100] 0.1273 0.0341 0.0241 0.0241 0.0216 ...
#>   ..- attr(*, "names")= chr [1:100] "MALAT1" "LYZ" "B2M" "ACTB" ...
#>  $ sigma_sq: Named num [1:100] 1.57e-03 2.05e-04 5.67e-05 7.81e-05 2.00e-04 ...
#>   ..- attr(*, "names")= chr [1:100] "MALAT1" "LYZ" "B2M" "ACTB" ...
#>  $ theta   : num 5.05
#> NULL
print(str(irls_list[['peak']])) # estimated mean and variance for peaks
#> List of 3
#>  $ mu      : Named num [1:250] 0.00559 0.00359 0.00423 0.00436 0.00282 ...
#>   ..- attr(*, "names")= chr [1:250] "chr11-65013281-65014841" "chr11-65026934-65027962" "chr11-65040583-65041780" "chr11-65083457-65084665" ...
#>  $ sigma_sq: Named num [1:250] -5.06e-06 1.65e-06 5.43e-07 3.94e-06 3.56e-06 ...
#>   ..- attr(*, "names")= chr [1:250] "chr11-65013281-65014841" "chr11-65026934-65027962" "chr11-65040583-65041780" "chr11-65083457-65084665" ...
#>  $ theta   : num 4.1
#> NULL