BayesPrism deconvolution of bulk data using an `screference`

Usage

bayesprism_deconvolute(
  bulk_data,
  scref,
  cache_path = NULL,
  outlier_cut = 0.01,
  outlier_fraction = 0.1,
  pseudo_min = 1e-08,
  ncores = parallel::detectCores()/2
)

Arguments

bulk_data: a matrix of genes-by-samples with bulk mixtures
scref: an object of class `screference`
cache_path: path to cache the results
outlier_cut, outlier.fraction: two floats used to filter genes in `bulk_data` whose expression fraction is greater than outlier.cut in more than outlier.fraction. Typically for dataset with reasonable quality control, very few genes will be filtered. Removal of outlier genes will ensure that the inference will not be dominated by outliers, which sometimes may be resulted from poor QC in mapping. See: [BayesPrism::new.prism()]
pseudo_min: float, the desired minimum value to replace zero after normalization. See: [BayesPrism::new.prism()].
n_cores: number of cores used for computation

Value

a tibble with deconvolution fractions

Note

Reference: Chu, T., Wang, Z., Pe’er, D. et al. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer 3, 505–517 (2022). https://doi-org.insb.bib.cnrs.fr/10.1038/s43018-022-00356-3 See also: https://github.com/Danko-Lab/BayesPrism