hierarchicalClassify {ADAPTS}

Hierarchical Deconvolution

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')