hierarchicalClassify: Hierarchical Deconvolution

In yxinyi2/ADAPTS1.0.3: Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells

Description

Deconvolve cell types based on clusters detected by an n-pass spillover matrix

Usage

hierarchicalClassify(
  sigMatrix,
  geneExpr,
  toPred,
  hierarchData = NULL,
  pdfDir = tempdir(),
  oneCore = FALSE,
  nPasses = 100,
  remZinf = TRUE,
  method = "DCQ",
  useRF = TRUE,
  incNonCluster = TRUE
)

Arguments

sigMatrix	The deconvolution matrix, e.g. LM22 or MGSM27
geneExpr	The source gene expression matrix used to calculate sigMatrix
toPred	The gene expression to ultimately deconvolve
hierarchData	The results of hierarchicalSplit OR hierarchicalSplit.sc (DEFAULT: NULL, ie hierarchicalSplit)
pdfDir	A fold to write the pdf file to (DEFAULT: tempdir())
oneCore	Set to TRUE to disable parallelization (DEFAULT: FALSE)
nPasses	The maximum number of iterations for spillToConvergence (DEFAULT: 100)
remZinf	Set to TRUE to remove any ratio with zero or infinity when generating gList (DEFAULT: FALSE)
method	One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)
useRF	Set to TRUE to use ranger random forests to build the seed matrix (DEFAULT: TRUE)
incNonCluster	Set to TRUE to include a 'nonCluster' in each of the sub matrices (DEFAULT: TRUE)

Value

a matrix of cell counts

Examples

#This toy example 
library(ADAPTS)
fullLM22 <- ADAPTS::LM22[1:30, 1:4]
smallLM22 <- fullLM22[1:25,] 

cellCounts <- hierarchicalClassify(sigMatrix=smallLM22, geneExpr=fullLM22, toPred=fullLM22, 
    oneCore=TRUE, nPasses=10, method='DCQ')

yxinyi2/ADAPTS1.0.3 documentation built on Aug. 5, 2020, 12:09 a.m.