SCDC_qc: Clustering QC

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

Clustering QC

Description

Single cells Clustering QC

Usage

SCDC_qc(
  sc.eset,
  ct.varname,
  sample,
  scsetname = "Single Cell",
  ct.sub,
  iter.max = 1000,
  nu = 1e-04,
  epsilon = 0.01,
  arow = NULL,
  qcthreshold = 0.7,
  generate.figure = T,
  ct.cell.size = NULL,
  cbPalette = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2",
    "#D55E00", "#CC79A7"),
  ...
)

Arguments

sc.eset	ExpressionSet object for single cells
ct.varname	variable name for 'cell type'
sample	variable name for subject/sample
scsetname	the name for the single cell dataset
ct.sub	a subset of cell types that are selected to construct basis matrix
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
arow	annotation of rows for pheatmap. Should be a variable name, like "sample" or "Subject".
qcthreshold	the probability threshold used to filter out questionable cells
generate.figure	logical. If generate the heatmap by pheatmap or not. default is TRUE.
ct.cell.size	default is NULL, which means the "library size" is calculated based on the data. Users can specify a vector of cell size factors corresponding to the ct.sub according to prior knowledge. The vector should be named: names(ct.cell.size input) should not be NULL.

Value

a list including: 1) a probability matrix for each single cell input; 2) a clustering QCed ExpressionSet object; 3) a heatmap of QC result.

meichendong/SCDC documentation built on Jan. 26, 2023, 5:08 p.m.