SCDC_prop_subcl_marker: Tree-guided proportion estimation

In crhisto/SCDC: Bulk RNA-seq deconvolution by scRNA-seq with multi-reference datasets

Description

Proportion estimation function for multi-subject case, and apply tree-guided deconvolution

Usage

SCDC_prop_subcl_marker(
  bulk.eset,
  sc.eset,
  ct.varname,
  fl.varname,
  sample,
  ct.sub = NULL,
  ct.fl.sub,
  iter.max = 3000,
  nu = 1e-04,
  epsilon = 0.001,
  weight.basis = T,
  truep = NULL,
  select.marker = T,
  markers = NULL,
  marker.varname = NULL,
  allgenes.fl = F,
  pseudocount.use = 1,
  LFC.lim = 0.5,
  parallelize = F,
  core_number = NULL,
  fix_number_genes = NULL,
  marker_gene_strategy = "boostrap_outliers",
  iteration.minimun_number_markers = 28,
  iteration.use_maximum = FALSE,
  iteration.maximo_genes = 35,
  iteration.use_final_foldchange = FALSE,
  bootstrap.sample_size = NULL,
  bootstrap.number = NULL,
  additional_genes = NULL,
  ...
)

Arguments

bulk.eset	ExpressionSet object for bulk samples
sc.eset	ExpressionSet object for single cell samples
ct.varname	variable name for 'cell types'
fl.varname	variable name for first-level 'meta-clusters'
sample	variable name for subject/samples
ct.sub	a subset of cell types that are selected to construct basis matrix
ct.fl.sub	'cell types' for first-level 'meta-clusters'
iter.max	the maximum number of iteration in WNNLS
nu	a small constant to facilitate the calculation of variance
epsilon	a small constant number used for convergence criteria
weight.basis	logical, use basis matrix adjusted by MVW, default is T.
truep	true cell-type proportions for bulk samples if known
select.marker	logical, select marker genes to perform deconvolution in tree-guided steps. Default is T.
markers	A set of marker gene that input manually to be used in deconvolution. If NULL, then
marker.varname	variable name of cluster groups when selecting marker genes. If NULL, then use ct.varname.
allgenes.fl	logical, use all genes in the first-level deconvolution
pseudocount.use	a constant number used when selecting marker genes, default is 1.
LFC.lim	a threshold of log fold change when selecting genes as input to perform Wilcoxon's test.
iteration.use_final_foldchange	TRUE/FALSE. If at the end the cluster has zero genes if this parameter is true, the boostraping is going to be calculated over the foldchange with <0.05, not with zero.

Value

Estimated proportion, basis matrix, predicted gene expression levels for bulk samples

crhisto/SCDC documentation built on Dec. 19, 2021, 6:19 p.m.