View source: R/onlyDeconAlgorithms.R
hierarchicalClassify | R Documentation |
Deconvolve cell types based on clusters detected by an n-pass spillover matrix
hierarchicalClassify( sigMatrix, geneExpr, toPred, hierarchData = NULL, pdfDir = tempdir(), oneCore = FALSE, nPasses = 100, remZinf = TRUE, method = "DCQ", useRF = TRUE, incNonCluster = TRUE )
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) |
a matrix of cell counts
#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')
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