missForest.par

Columns are features, Rows examples. The data with NA values. 'xmis' in missForest

dataMat

split on 'forests' or 'variables' (DEFAULT: 'variables')

parallelize

newMatrix &lt;- missForest.par(dataMat)

Augments existing or de-novo cell-type signature matrices to deconvolve bulk gene expression data.
Useful for building signature matrices from single cell RNAseq data, determine cell type deconvolution
spillover, and hierarchical deconvolution to use spillover to increase deconvolution accuracy. Please cite:
Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for
Tissue Specific cells <doi:10.1101/633958>. This package expands on the techniques outlined in Newman AM,
Liu CL, Green MR, Gentles AJ, Feng W, Xu Y,
Hoang CD, Diehn M, Alizadeh, AA (2015) <doi:10.1038/nmeth.3337>'s Nature Methods paper:
'Robust enumeration of cell subsets from tissue expression profiles' to allow a user to easily add
their own cell types (e.g. a tumor specific cell type) to Newman's LM22 or other signature matrix.

Samuel Danziger

ADAPTS

Automated Deconvolution Augmentation of Profiles for Tissue
Specific Cells

missForest.par function

Use parallel missForest to impute missing values.
  This wrapper is required because missForest crashed if you have more cores than variables.
  This will default to no parellelization for Windows — missForest.par

Use parallel missForest to impute missing values.
  This wrapper is required because missForest crashed if you have more cores than variables.
  This will default to no parellelization for Windows

missForest.par: Use parallel missForest to impute missing values. This wrapper is required because missForest crashed if you have more cores than variables. This will default to no parellelization for Windows

Description

Usage

Arguments

Value

Examples