Help for package ADAPTS

Type:

Package

Title:

Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells

Version:

1.0.22

Author:

Samuel A Danziger

Maintainer:

Samuel A Danziger <sam.danziger@gmail.com>

Bristol-Myers Squibb

Description:

Tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data. Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.1.2

Depends:

R (≥ 3.3.0)

Imports:

missForest, e1071, ComICS, pheatmap, doParallel, utils, quantmod, preprocessCore, pcaMethods, foreach, nnls, ranger

Suggests:

R.rsp, DeconRNASeq, WGCNA

VignetteBuilder:

R.rsp

NeedsCompilation:

Packaged:

2022-09-14 02:37:08 UTC; sdanzige

Repository:

CRAN

Date/Publication:

2022-09-14 06:50:14 UTC

Make an augmented signature matrix

Description

Build an augmented signature matrix from an initial signature matrix, source data, and a list of differentially expressed genes (gList). The user might want to modify gList to make certain that particular genes are included in the matrix. The algorithm will be to add one additional gene from each new cell type Record the condition number, and plot those. Will only consider adding rows shared by fullData and newData

newMatData <- AugmentSigMatrix(origMatrix, fullData, newData, gList)

Usage

AugmentSigMatrix(
  origMatrix,
  fullData,
  newData,
  gList,
  nGenes = 1:100,
  plotToPDF = TRUE,
  imputeMissing = TRUE,
  condTol = 1.01,
  postNorm = FALSE,
  minSumToRem = NA,
  addTitle = NULL,
  autoDetectMin = FALSE,
  calcSpillOver = FALSE,
  pdfDir = tempdir(),
  plotIt = TRUE
)

Arguments

origMatrix

The original signature matrix

fullData

The full data for the signature matrix

newData

The new data to add signatures from

gList

The ordered list of genes from running rankByT() on newData. NOTE: best genes at the bottom!!

nGenes

The number of additional genes to consider (DEFAULT: 1:100)

plotToPDF

Plot the output condition numbers to a pdf file. (DEFAULT: TRUE)

imputeMissing

Set to TRUE to impute missing values. NOTE: adds stoachasiticity (DEFAULT: TRUE)

condTol

Setting higher tolerances will result in smaller numbers extra genes. 1.00 minimizes compliment number (DEFAULT: 1.00)

postNorm

Set to TRUE to normalize new signatures to match old signatures. (DEFAULT: FALSE)

minSumToRem

Set to non-NA to remove any row with the sum(abs(row)) < minSumToRem (DEFAULT: NA)

addTitle

An optional string to add to the plot and savefile (DEFAULT: NULL)

autoDetectMin

Set to true to automatically detect the first local minima. GOOD PRELIMINARY RESULTS (DEAFULT: FALSE)

calcSpillOver

Use the training data to calculate a spillover matrix (DEFAULT: FALSE)

pdfDir

A fold to write the pdf file to if plotToPDF=TRUE (DEFAULT: tempdir())

plotIt

Set to FALSE to suppress non-PDF plotting (DEFAULT: TRUE)

Value

an augmented cell type signature matrix

Examples

#This toy example treats the LM22 deconvolution matrix as if it were all of the data
#  For a real example, look at the vignette or comments in exprData, fullLM22, small LM22
library(ADAPTS)
fullLM22 <- ADAPTS::LM22[1:200, 1:8]
#Make a fake signature matrix out of 100 genes and the first 8 cell types
smallLM22 <- fullLM22[1:100, 1:8] 

#Make fake data representing two replicates of purified Mast.cells 
exprData <- ADAPTS::LM22[1:200, c("Mast.cells.resting","Mast.cells.activated")]
colnames(exprData) <- c("Mast.cells", "Mast.cells")

#Fake source data with replicates for all purified cell types.
#  Note in this fake data set, many cell types have exactly one replicate
fakeAllData <- cbind(fullLM22, as.data.frame(exprData)) 
gList <- rankByT(geneExpr = fakeAllData, qCut=0.3, oneCore=TRUE)

newSig <- AugmentSigMatrix(origMatrix=smallLM22, fullData=fullLM22, newData=exprData, 
    gList=gList, plotToPDF=FALSE)

Leukocyte 22 data matrix

Description

Newman et al.'s 2015 22 leukocyte signature matrix.

Usage

data("LM22")

Format

A data frame with 547 observations on the following 22 variables.

B.cells.naive: a numeric vector
B.cells.memory: a numeric vector
Plasma.cells: a numeric vector
T.cells.CD8: a numeric vector
T.cells.CD4.naive: a numeric vector
T.cells.CD4.memory.resting: a numeric vector
T.cells.CD4.memory.activated: a numeric vector
T.cells.follicular.helper: a numeric vector
T.cells.regulatory..Tregs.: a numeric vector
T.cells.gamma.delta: a numeric vector
NK.cells.resting: a numeric vector
NK.cells.activated: a numeric vector
Monocytes: a numeric vector
Macrophages.M0: a numeric vector
Macrophages.M1: a numeric vector
Macrophages.M2: a numeric vector
Dendritic.cells.resting: a numeric vector
Dendritic.cells.activated: a numeric vector
Mast.cells.resting: a numeric vector
Mast.cells.activated: a numeric vector
Eosinophils: a numeric vector
Neutrophils: a numeric vector

Source

Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015). https://media.nature.com/original/nature-assets/nmeth/journal/v12/n5/extref/nmeth.3337-S2.xls

Examples

data(LM22)
heatmap(as.matrix(LM22))

Licenses required by Celgene legal

Description

This software is covered by the MIT license. Celgene legal thought it was wise to break the license up into the two license files included in this list.

Usage

data("Licenses")

Format

A data frame with 0 observations on the following 2 variables.

x: a numeric vector
y: a numeric vector

Source

https://www.r-project.org/Licenses/MIT

Examples

data(Licenses)
str(Licenses)

Myeloma Genome Signature Matrix 27

Description

Newman et al's 2015 plus 5 myeloma specific cell types. Osteoclasts, Adipocytes, Osteoblasts, Multiple Myeloma Plasma Cells, and Plasma Memory Cells

Usage

data("MGSM27")

Format

A data frame with 601 observations on the following 27 variables.

B.cells.naive: a numeric vector
B.cells.memory: a numeric vector
Plasma.cells: a numeric vector
T.cells.CD8: a numeric vector
T.cells.CD4.naive: a numeric vector
T.cells.CD4.memory.resting: a numeric vector
T.cells.CD4.memory.activated: a numeric vector
T.cells.follicular.helper: a numeric vector
T.cells.regulatory..Tregs.: a numeric vector
T.cells.gamma.delta: a numeric vector
NK.cells.resting: a numeric vector
NK.cells.activated: a numeric vector
Monocytes: a numeric vector
Macrophages.M0: a numeric vector
Macrophages.M1: a numeric vector
Macrophages.M2: a numeric vector
Dendritic.cells.resting: a numeric vector
Dendritic.cells.activated: a numeric vector
Mast.cells.resting: a numeric vector
Mast.cells.activated: a numeric vector
Eosinophils: a numeric vector
Neutrophils: a numeric vector
MM.plasma.cell: a numeric vector
osteoblast: a numeric vector
osteoclast: a numeric vector
PlasmaMemory: a numeric vector
adipocyte: a numeric vector

Details

MGSM27 as constructed for Identifying a High-risk Cellular Signature in the Multiple Myeloma Bone Marrow Microenvironment.

Source

https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3732/ https://www.ebi.ac.uk/arrayexpress/experiments/E-MEXP-3711/ https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4152/

Examples

data(MGSM27)
heatmap(as.matrix(MGSM27))

Support vector machine deconvolution

Description

Use SVMDECONV to estimate the cell count percentage David L Gibbs, dgibbs@systemsbiology.org June 9, 2017

v-SVR is applied with a linear kernel to solve for f, and the best result from three values of v = 0.25, 0.5, 0.75 is saved, where ‘best’ is defined as the lowest root mean squared error between m and the deconvolution result, f x B.

Our current implementation executes v-SVR using the ‘svm’ function in the R package, ‘e1071’.

w2 <- SVMDECON(m, B)

Usage

SVMDECON(m, B)

Arguments

m

a matrix represenging the mixture (genes X 1 sample)

B

a matrix representing the references (genes X cells), m should be subset to match B

Value

A matrix with cell type estimates for each samples

Build a deconvolution seed matrix, add the proportional option

Description

Use ranger to select features and build a genesInSeed gene matrix

Usage

buildSeed(
  trainSet,
  genesInSeed = 200,
  groupSize = 30,
  randomize = TRUE,
  num.trees = 1000,
  plotIt = TRUE,
  trainSet.3sam = NULL,
  trainSet.30sam = NULL,
  proportional = FALSE
)

Arguments

trainSet

Each row is a gene, and each column is an example of a particular cell type, ie from single cell data

genesInSeed

The maximum number of genes in the returned seed matrix (DEFAULT: 200)

groupSize

The number of groups to break the trainSet into by ADAPTS::scSample (DEFAULT: 30)

randomize

Set to TRUE randomize the sets selected by ADAPTS::scSample (DEFAULT: TRUE)

num.trees

The number of trees to be used by ranger (DEFAULT: 1000)

plotIt

Set to TRUE to plot (DEFAULT: TRUE)

trainSet.3sam

Optional pre-calculated ADAPTS::scSample(trainSet, groupSize = 3) (DEFAULT: NULL)

trainSet.30sam

Optional pre-calculated ADAPTS::scSample(trainSet, groupSize=groupSize, randomize=randomize) (DEFAULT: NULL)

proportional

Set to true to make the training set cell type proportional. Ignores group size (DEFAULT: FALSE)

Value

A list with condition numbers and gene lists

Examples

library(ADAPTS)
ct1 <- runif(1000, 0, 100)
ct2 <- runif(1000, 0, 100)
dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2)
rownames(dataMat) <- make.names(rep('gene', nrow(dataMat)), unique=TRUE)
noise <- matrix(runif(nrow(dataMat)*ncol(dataMat), -2, 2), nrow = nrow(dataMat), byrow = TRUE)
dataMat <- dataMat + noise
newSigMat <- buildSeed(trainSet=dataMat)

Build a spillover matrix

Description

Build a spillover matrix, i.e. what do purified samples deconvolve as?

spillExpr <- buildSpilloverMat(refExpr, geneExpr, method='DCQ')

Usage

buildSpilloverMat(refExpr, geneExpr, method = "DCQ")

Arguments

refExpr

The deconvolution matrix, e.g. LM22 or MGSM27

geneExpr

The full gene expression for purified cell types. Multiple columns (examples) for each column in the reference expr.

method

One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)

Value

A spillover matrix showing how purified cell types deconvolve

Examples

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

spillover <- buildSpilloverMat(refExpr=smallLM22, geneExpr=fullLM22, method='DCQ')

Calculate prediction accuracy

Description

Calculate correlation coeffifients, p-Values, MAE, RMSE for deconvolution predictions

Usage

calcAcc(estimates, reference)

Arguments

estimates

The estimated cell percentages

reference

The reference cell percentages

Value

a list with a multiple sets

Examples

estimates <- sample(c(runif(8), 0 ,0))
estimates <- 100 * estimates / sum(estimates)
reference <- sample(c(runif(7), 0 , 0, 0))
reference <- 100 * reference / sum(reference)
calcAcc(estimates, reference)

Cluster with spillover

Description

Build clusters based on n-pass spillover matrix

Usage

clustWspillOver(
  sigMatrix,
  geneExpr,
  nPasses = 100,
  deconMatrices = NULL,
  method = "DCQ"
)

Arguments

sigMatrix

The deconvolution matrix, e.g. LM22 or MGSM27

geneExpr

The source gene expression matrix used to calculate sigMatrix.

nPasses

The maximum number of iterations for spillToConvergence (DEFAULT: 100)

deconMatrices

Optional pre-computed results from spillToConvergence (DEFAULT: NULL)

method

One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)

Value

Cell types grouped by cluster

Examples

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

clusters <- clustWspillOver(sigMatrix=smallLM22, geneExpr=fullLM22, nPasses=10)

Collapse cell types

Description

Collapse the cell types (in rows) to super-classes Including MGSM36 cell types

Usage

collapseCellTypes(cellCounts, method = "Pheno4")

Arguments

cellCounts

A matrix with cell counts

method

The method for combining cell types ('Default: 'Pheno2') Pheno1: Original cell-type based combinations Pheno2: Original cell-type based combinations, omitting Macrophages Pheno3: Alt Phenotype definitions based on WMB deconvolution correlations Pheno4: Consensus cell types Pheno5: Consensus cell types, combined myeloma & plasma Spillover1: Empirical combinations based on compToLM22source Spillover2: More agressive combination based on empirical combinations based on compToLM22source Spillover3: Combinations determined by spillToConvergence on 36 cell types

Value

a cell estimate matrix with the names changed

Examples

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

cellEst <- estCellPercent.DCQ(refExpr=smallLM22, geneExpr=fullLM22)
collapseCounts <- collapseCellTypes(cellCounts=cellEst)

Deconvolve with an n-pass spillover matrix

Description

curExpr <- estCellCounts.nPass(sigMatrix, deconMatrices)

Usage

estCellCounts.nPass(geneExpr, deconMatrices, method = "DCQ")

Arguments

geneExpr

The gene expression matrix

deconMatrices

The results from spillToConvergence()

method

One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)

Value

An estimate of cell counts

Examples

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

deconMatrices <- spillToConvergence(sigMatrix=smallLM22, geneExpr=fullLM22, nPasses=10)
cellCounts <- estCellCounts.nPass(geneExpr=fullLM22, deconMatrices=deconMatrices, method='DCQ')

Wrapper for deconvolution methods

Description

A wrapper function to call any of the estCellPercent functions Modified on June 16th 2021 to quantile normalize the geneExpr data to match refExpr Set preNormalize to FALSE for previous behavior.

Usage

estCellPercent(
  refExpr,
  geneExpr,
  preNormalize = TRUE,
  verbose = TRUE,
  method = "DCQ",
  ...
)

Arguments

refExpr

a data frame representing immune cell expression profiles. Each row represents an expression of a gene, and each column represents a different immune cell type. colnames contains the name of each immune cell type and the rownames includes the genes' symbol. The names of each immune cell type and the symbol of each gene should be unique. Any gene with missing expression values must be excluded.

geneExpr

a data frame representing RNA-seq or microarray gene-expression profiles of a given complex tissue. Each row represents an expression of a gene, and each column represents a different experimental sample. colnames contain the name of each sample and rownames includes the genes' symbol. The name of each individual sample and the symbol of each gene should be unique. Any gene with missing expression values should be excluded.

preNormalize

Set to TRUE to quantile normalize geneExpr to match refExpr (DEFAULT: TRUE)

verbose

Set to TRUE to echo the results of parameters (DEFAULT: TRUE)

method

One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)

...

Parameters for estCellPercent.X (e.g. number_of_repeats for .DCQ)

Value

A matrix with cell type estimates for each samples

Examples

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

cellEst <- estCellPercent(refExpr=smallLM22, geneExpr=fullLM22, preNormalize=FALSE, verbose=TRUE)

DCQ Deconvolution

Description

Use DCQ to estimate the cell count percentage Requires installation of package 'ComICS' To Do: Also report the standard deviation as a confidence metric

Usage

estCellPercent.DCQ(
  refExpr,
  geneExpr,
  marker_set = NULL,
  number_of_repeats = 10,
  alpha = 0.05,
  lambda = 0.2
)

Arguments

refExpr

geneExpr

marker_set

data frames of one column, that includes a preselected list of genes that likely discriminate well between the immune-cell types given in the reference data. (DEFAULT: NULL, i.e. one for each gene in the refExpr)

number_of_repeats

using one repeat will generate only one output model. Using many repeats, DCQ calculates a collection of models, and outputs the average and standard deviation for each predicted relative cell quantity. (DEFAULT: 1)

alpha

The elasticnet mixing parameter, with 0 <= alpha <= 1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. (DEFAULT: 0.05)

lambda

A minimum value for the elastic net lambda parameter (DEFAULT: 0.2)

Value

A matrix with cell type estimates for each samples

Examples

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

cellEst <- estCellPercent.DCQ(refExpr=smallLM22, geneExpr=fullLM22)

DeconRNASeq deconvolution

Description

Use DeconRNASeq to estimate the cell count percentage Performs with similar effectiveness as DCQ, but identifies different proportions of cell-types Requires installation of package 'DeconRNASeq': source("https://bioconductor.org/biocLite.R") biocLite("DeconRNASeq")

<joseph.szustakowski@novartis.com> TGJDS (2013). DeconRNASeq: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data. R package version 1.18.0.

cellEst <- estCellPercent.DeconRNASeq(refExpr, geneExpr, marker_set=NULL)

Usage

estCellPercent.DeconRNASeq(refExpr, geneExpr, marker_set = NULL)

Arguments

refExpr

geneExpr

marker_set

Value

A matrix with cell type estimates for each samples

Examples


#This toy example, donttest due to performance issues in windows development build 
library(ADAPTS)
fullLM22 <- ADAPTS::LM22[1:30, 1:4]
smallLM22 <- fullLM22[1:25,] 

cellEst <- estCellPercent.DeconRNASeq(refExpr=smallLM22, geneExpr=fullLM22)

Non-negative least squares deconvolution

Description

Use non-negative least squares regression to deconvolve a sample This is going to be to simple to be useful This might be more interesting if I used non-positive least squares to detect 'other'

Usage

estCellPercent.nnls(refExpr, geneExpr)

Arguments

refExpr

geneExpr

Value

A matrix with cell type estimates for each samples

Examples

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

cellEst <- estCellPercent.nnls(refExpr=smallLM22, geneExpr=fullLM22)

WGCNA::proportionsInAdmixture deconvolution

Description

Use R function proportionsInAdmixture to estimate the cell count percentage Uses the 'WGCNA' package

cellEst <- estCellPercent.proportionsInAdmixture(refExpr)

Usage

estCellPercent.proportionsInAdmixture(refExpr, geneExpr, marker_set = NULL)

Arguments

refExpr

geneExpr

marker_set

Value

A matrix with cell type estimates for each samples

Examples


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

cellEst <- estCellPercent.proportionsInAdmixture(refExpr=smallLM22, geneExpr=fullLM22)

Estimate cell percentage from spillover

Description

Use a spillover matrix to deconvolve a samples

Usage

estCellPercent.spillOver(spillExpr, refExpr, geneExpr, method = "DCQ", ...)

Arguments

spillExpr

A spill over matrix, as calculated by buildSpilloverMat(). (e.g. LM22.spillover.csv.gz)

refExpr

geneExpr

method

One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)

...

Parameters for estCellPercent.X (e.g. number_of_repeats for .DCQ)

Value

a matrix of estimate cell type percentages in samples

Examples

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

spillover <- buildSpilloverMat(refExpr=smallLM22, geneExpr=fullLM22) 
cellEst <- estCellPercent.spillOver(spillExpr=spillover, refExpr=smallLM22, geneExpr=fullLM22)

SVMDECON deconvolution

Description

Use SVMDECON to estimate the cell count percentage Performs considerably worse in deconvolution than DCQ

cellEst <- estCellPercent.svmdecon(refExpr, geneExpr)

Usage

estCellPercent.svmdecon(
  refExpr,
  geneExpr,
  marker_set = NULL,
  useOldVersion = F,
  progressBar = T
)

Arguments

refExpr

geneExpr

marker_set

useOldVersion

Set the TRUE to 2^ the data (DEFAULT: FALSE)

progressBar

Set to TRUE to show a progress bar (DEFAULT: TRUE)

Value

A matrix with cell type estimates for each samples #This toy example library(ADAPTS) fullLM22 <- ADAPTS::LM22[1:30, 1:4] smallLM22 <- fullLM22[1:25,]

cellEst <- estCellPercent.svmdecon(refExpr=smallLM22, geneExpr=fullLM22)

Find out at which iteration the results converge, i.e. the mean results are stable.

Description

Find out at which iteration the results converge, i.e. the mean results are stable.

Usage

findConvergenceIter(curSeq, changePer = 1, winSize = 5)

Arguments

curSeq

A sequence of results that generated from each iteration of the loop

changePer

The maximum percentage of change allowed

winSize

The window size for mean calculation

Value

The minimum number of iterations needed for the results to converge

Build a gList using random forest

Description

Use ranger to select features and build a genesInSeed gene matrix

Usage

gListFromRF(trainSet, oneCore = FALSE)

Arguments

trainSet

Each row is a gene, and each column is an example of a particular cell type, e.g. ADAPTS::scSample(trainSet, groupSize=30)

oneCore

SEt to TRUE to disable multicore (DEFAULT: FALSE)

Value

A cell specific geneList for ADAPTS::AugmentSigMatrix()

Examples

library(ADAPTS)
ct1 <- runif(1000, 0, 100)
ct2 <- runif(1000, 0, 100)
dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2)
rownames(dataMat) <- make.names(rep('gene', nrow(dataMat)), unique=TRUE)
noise <- matrix(runif(nrow(dataMat)*ncol(dataMat), -2, 2), nrow = nrow(dataMat), byrow = TRUE)
dataMat <- dataMat + noise
gList <- gListFromRF(trainSet=dataMat, oneCore=TRUE)

Get f1 / mcc

Description

Get f1 / mcc and other accuracy measurements for binary predictions. Provide either an estimate and reference vector e.g. getF1mcc(estimate, reference) Or TPs, FPs, etc. e.g. getF1mcc(tps=3, fps=4, tns=7, fns=2)

Usage

getF1mcc(
  estimate = NULL,
  reference = NULL,
  tps = NULL,
  fps = NULL,
  tns = NULL,
  fns = NULL
)

Arguments

estimate

A binary vector of predictions

reference

a binary vector of actual values

tps

The number of TPs

fps

The number of FPs

tns

The number of TNs

fns

The number of FNs

Value

A vector with sensitivity, specificity, fpr, fdr, f1, agreement, p.value, mcc, and mcc.p

Examples

estimates <- sample(c(runif(8), 0 ,0))
reference <- sample(c(runif(7), 0 , 0, 0))
accuracyStats <- getF1mcc(estimate=estimates>0, reference=reference>0)

LM22 look up table

Description

Load a map of cell type names

Usage

getLM22cells()

Value

a map of cell types names

Examples

cellMap <- getLM22cells()

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')

Build hierarchical cell clusters.

Description

Attempt to deconvolve cell types by building a hierarchy of cell types using spillToConvergence to determine cell types that are not signficantly different. First deconvolve those clusters of cell types. Deconvolution matrices are then built to separate the cell types that formerly could not be separated.

Usage

hierarchicalSplit(
  sigMatrix,
  geneExpr,
  oneCore = FALSE,
  nPasses = 100,
  deconMatrices = NULL,
  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

oneCore

Set to TRUE to disable parallelization (DEFAULT: FALSE)

nPasses

The maximum number of iterations for spillToConvergence (DEFAULT: 100)

deconMatrices

Optional pre-computed results from spillToConvergence (DEFAULT: NULL)

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 list of clusters and a list of signature matrices for breaking those clusters

Examples

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

clusters <- hierarchicalSplit(sigMatrix=smallLM22, geneExpr=fullLM22, oneCore=TRUE, nPasses=10,
    deconMatrices=NULL, remZinf=TRUE, method='DCQ', useRF=TRUE, incNonCluster=TRUE)

Load MGSM27

Description

Load the MGSM27 signature matrix

Usage

loadMGSM27()

Value

The MGSM27 signature matrix from Identifying a High-risk Cellular Signature in the Multiple Myeloma Bone Marrow Microenvironment

Examples

MGSM27 <- loadMGSM27()

LM22 to xCell LUT

Description

Load the LM22 xCell map

Usage

loadModMap()

Value

A map between xCell cell type names and LM22 cell type names

Examples

xcellMap <- loadModMap()

Loop testAllSigMatrices until convergence

Description

Iteratively call testAllSigMatrices numLoops times with the option to fast stop if correlation, correlation spear, mae and rmse all converge

Usage

loopTillConvergence(
  numLoops,
  fastStop,
  exprData,
  changePer,
  handMetaCluster,
  testOnHalf,
  condTol = 1.01
)

Arguments

numLoops

The number of iterations. Set to null to loop until results converge.

fastStop

Set to TRUE to break the loop when correlation, correlation spear, mae and rmse all converge

exprData

The single cell matrix

changePer

The maximum percentage of change allowed for convergence

handMetaCluster

A List of pre-defined meta clusters. Set to NULL to automatically group indistinguishable cells into same cluster use clustWspillOver (DEFAULT: NULL)

testOnHalf

Set to TRUE to leave half the data as a test set to validate all the matrices

condTol

The tolerance in the reconstruction algorithm. 1.0 = no tolerance, 1.05 = 5% tolerance (DEFAULT: 1.01)

Value

A list of results generated from all the iterative calls of testAllSigMatrices

Examples

ct1 <- runif(1000, 0, 100)
ct2 <- runif(1000, 0, 100)
ct3 <- runif(1000, 0, 100)
ct4 <- runif(1000, 0, 100)
dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2, ct3, ct3, ct3,ct3,ct4,ct4)
rownames(dataMat) <- make.names(rep('gene', nrow(dataMat)), unique=TRUE)
noise <- matrix(runif(nrow(dataMat)*ncol(dataMat), -2, 2), nrow = nrow(dataMat), byrow = TRUE)
dataMat <- dataMat + noise
#options(mc.cores=2)
#  This is a meta-function that calls other functions, 
#  The execution speed is too slow for the CRAN automated check
#loopTillConvergence(numLoops=10, fastStop=TRUE, exprData=dataMat, 
#    changePer=10,handMetaCluster=NULL, testOnHalf=TRUE)

Make a GSVA genelist

Description

Provide a gList and signature matrix with matched cell types to get signatures gene lists for GSVA and similar algorithms. gList=NULL select highest genes for each cell type, minimum of 3.

Usage

matrixToGenelist(sigMat, gList = NULL)

Arguments

sigMat

A signature matrix such as from ADAPTS::AugmentSigMatrix()

gList

A list of prioritized genes such as from ADAPTS::gListFromRF() (DEFAULT:NULL)

Value

A list of genes for each cell types musually in sigMat and gList

Examples

library(ADAPTS)
ct1 <- runif(1000, 0, 100)
ct2 <- runif(1000, 0, 100)
dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2)
rownames(dataMat) <- make.names(rep('gene', nrow(dataMat)), unique=TRUE)
noise <- matrix(runif(nrow(dataMat)*ncol(dataMat), -2, 2), nrow = nrow(dataMat), byrow = TRUE)
dataMat <- dataMat + noise
gList <- ADAPTS::gListFromRF(trainSet=dataMat, oneCore=TRUE)
newSigMat <- ADAPTS::buildSeed(trainSet=dataMat, plotIt=FALSE)
geneLists <- matrixToGenelist(sigMat=newSigMat, gList=gList)

A meta analysis for the results from multiple iterations

Description

Calculate the mean and the standard deviation of the results from all the iterations, and also test for convergence by

Usage

meanResults(allResList, changePer = 1)

Arguments

allResList

A list of results generated from all the iterative calls of testAllSigMatrices

changePer

The maximum percentage of change allowed for convergence

Value

The mean and standard deviation of all the results, along with the number of iterations needed for the results to converge. A meta analysis for the results from multiple iterations

The mean and standard deviation of all the results, along with the number of iterations needed for the results to converge.

Use parallel missForest to impute missing values.

Description

This wrapper is helpful because missForest crashes if you have more cores than variables. This will default to no parellelization for Windows

newMatrix <- missForest.par(dataMat)

Usage

missForest.par(dataMat, parallelize = "variables")

Arguments

dataMat

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

parallelize

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

Value

a matrix including imputed values

Examples

library(ADAPTS)
LM22 <- ADAPTS::LM22
LM22[2,3] <- as.numeric(NA) #Make some missing data to impute
LM22.imp <- missForest.par(LM22)

Plot condition numbers

Description

Plot the condition numbers during the growing and shrinking of signature matrices.

bonusPoints <- data.frame(legText = c('Unagumented Signature Matrix', 'Minimum Smoothed Condition Number', 'Best Augmented Signature Matrix'), pchs = c('o', 'x', 'x'), cols = c('red', 'purple', 'blue'), kappa = c(10, 15, 20), nGene = c(5, 10, 15))

Usage

plotKappas(
  kappas,
  nGenes,
  smData = NULL,
  titleStr = "Shrink Signature Matrix",
  bonusPoints = NULL,
  maxCond = 100
)

Arguments

kappas

The condition numbers to plot

nGenes

The number of genes associated with each kapp

smData

Smoothed data to plot as a green line (DEFAULT: NULL)

titleStr

The title of the plot (DEFAULT: 'Shrink Signature Matrix')

bonusPoints

Set to plot additional points on the plot, see description (DEFAULT: NULL)

maxCond

Cap the condition number to maxCond (DEFAULT: 100)

Value

a matrix including imputed values

Examples

nGenes <- 1:300
kappas <- log(abs(nGenes-250))
kappas[is.infinite(kappas)] <- 0
kappas <- kappas+runif(300, 0, 1)
smData <- stats::smooth(kappas)
bonusPoints <- data.frame(legText = 'Minimum Smoothed ', pchs='x', cols='purple', 
kappa=min(smData), nGenes=nGenes[which.min(smData)])
plotKappas(kappas=kappas, nGenes=nGenes, smData=smData, bonusPoints=bonusPoints, maxCond=100)

Rank genes for each cell type

Description

Use a t-test to rank to features for each cell type

gList <- rankByT(geneExpr, qCut=0.3)

Usage

rankByT(
  geneExpr,
  qCut = 0.3,
  oneCore = FALSE,
  secondPval = TRUE,
  remZinf = FALSE,
  reqRatGT1 = FALSE
)

Arguments

geneExpr

The gene expression data

qCut

(DEFAULT: 0.3)

oneCore

Set to TRUE to disable paralellization (DEFAULT: FALSE)

secondPval

Set to TRUE to use p-Values as a second sort criteria (DEFAULT: TRUE)

remZinf

Set to TRUE to remove any ratio with zero or infinity. Good for scRNAseq. (DEFAULT: FALSE)

reqRatGT1

Set to TRUE to remove any gene with a ratio with less than 1. Good for scRNAseq. (DEFAULT: FALSE)

Value

a list of cell types with data frames ranking genes

Examples

#This toy example treats the LM22 deconvolution matrix as if it were all of the data
#  For a real example, look at the vignette or comments in exprData, fullLM22, small LM22
library(ADAPTS)
fullLM22 <- ADAPTS::LM22[1:200, 1:8]
#Make a fake signature matrix out of 100 genes and the first 8 cell types
smallLM22 <- fullLM22[1:100, 1:8] 

#Make fake data representing two replicates of purified Mast.cells 
exprData <- ADAPTS::LM22[1:200, c("Mast.cells.resting","Mast.cells.activated")]
colnames(exprData) <- c("Mast.cells", "Mast.cells")

#Fake source data with replicates for all purified cell types.
#  Note in this fake data set, many cell types have exactly one replicate
fakeAllData <- cbind(fullLM22, as.data.frame(exprData)) 
gList <- rankByT(geneExpr = fakeAllData, qCut=0.3, oneCore=TRUE, reqRatGT1=FALSE)

Make an Augmented Signature Matrix

Description

With the ADAPTSdata packge, it will use the full LM22 data matrix and add a few additional genes to cover osteoblasts, osteoclasts, Plasma.memory, MM. In many ways this is just a convenient wrapper for AugmentSigMatrix that calculates and caches a gList.

Usage

remakeLM22p(
  exprData,
  fullLM22,
  smallLM22 = NULL,
  plotToPDF = TRUE,
  condTol = 1.01,
  postNorm = TRUE,
  autoDetectMin = FALSE,
  pdfDir = tempdir(),
  oneCore = FALSE,
  cache_gList = TRUE
)

Arguments

exprData

The gene express data to use to augment LM22, e.g. ADAPTSdata::addMGSM27

fullLM22

LM22 data with all genes. Available in ADAPTSdata2::fullLM22

smallLM22

The small LM22 matrix, if it includes new cell types in exprData those will not be overwritten (DEFAULT: NULL, i.e. buildLM22plus(useLM22genes = TRUE)

plotToPDF

TRUE: pdf, FALSE: standard display (DEFAULT: TRUE)

condTol

The tolerance in the reconstruction algorithm. 1.0 = no tolerance, 1.05 = 5% tolerance (DEFAULT: 1.01)

postNorm

Set to TRUE to normalize new signatures to match old signatures. To Do: Redo Kappa curve? (DEFAULT: TRUE)

autoDetectMin

Set to true to automatically detect the first local minima. GOOD PRELIMINARY RESULTS (DEAFULT: FALSE)

pdfDir

A fold to write the pdf file to if plotToPDF=TRUE (DEFAULT: tempdir())

oneCore

Set to TRUE to disable parallelization (DEFAULT: FALSE)

cache_gList

Set to TRUE to cache slow gList calculations (DEFAULT: TRUE)

Value

a cell type signature matrix

Examples

#This toy example treats the LM22 deconvolution matrix as if it were all of the data
#  For a real example, look at the vignette or comments in exprData, fullLM22, small LM22
library(ADAPTS)
fullLM22 <- ADAPTS::LM22[1:200, 1:8]
#Make a fake signature matrix out of 100 genes and the first 8 cell types
smallLM22 <- fullLM22[1:100, 1:8] 

#Make fake data representing two replicates of purified Mast.cells types 
exprData <- ADAPTS::LM22[1:200, c("Mast.cells.resting","Mast.cells.activated")]
colnames(exprData) <- c("Mast.cells", "Mast.cells")
newSig <- remakeLM22p(exprData=exprData, fullLM22=fullLM22, smallLM22=smallLM22, 
    plotToPDF=FALSE, oneCore=TRUE, cache_gList=FALSE)

Build groupSize pools according to cellIDs

Description

This function is intended to collapse many single cells into 3 (groupsize) groups with the average count across all cells in each of the groups. These groups can then be used to perform a t-test (for example) between the 3 groups of CellX with 3 groups of CellY

Usage

scSample(
  RNAcounts,
  cellIDs = colnames(RNAcounts),
  groupSize = 3,
  randomize = TRUE,
  mc.cores = 1
)

Arguments

RNAcounts

The single cell matrix

cellIDs

A vector will cell types for each column in scCountMatrix (DEFAULT: colnames(RNAcounts))

groupSize

The number of sets to break it up into (DEFAULT: 3)

randomize

Set to TRUE to randomize the sets (DEFAULT: TRUE)

mc.cores

The number of cores to use (DEFAULT: 1)

Value

a list with a multiple sets

Examples

RNAcounts <- matrix(0, nrow=10, ncol=100)
rownames(RNAcounts) <- make.names(rep('Gene', nrow(RNAcounts)), unique=TRUE)
colnames(RNAcounts) <- make.names(c('CellX', rep('CellY', 39), 
rep('CellZ', 30), rep('CellB', 30)), unique=TRUE)
RNAcounts[, grepl('CellY', colnames(RNAcounts))] <- 1
RNAcounts[, grepl('CellZ', colnames(RNAcounts))] <- 2
RNAcounts[, grepl('CellB', colnames(RNAcounts))] <- 3
scSample(RNAcounts, groupSize=3)

Calculate conditions numbers for signature subsets

Description

Remove genes by chunks by picking those the most improve the condition number. Will set any infinite condition numbers to max(kappas[!is.infinite(kappas)])+1 Return the condition numbers with their gene lists

Usage

shrinkByKappa(
  sigMatrix,
  numChunks = NULL,
  verbose = TRUE,
  plotIt = TRUE,
  singleCore = FALSE,
  fastStop = TRUE
)

Arguments

sigMatrix

The original signature matrix

numChunks

The number of groups of genes to remove (DEFAULT: NULL)

verbose

Print out the current chunk as is it's being calculated (DEFAULT: NULL)

plotIt

The title of the plot (DEFAULT: TRUE)

singleCore

Set to FALSE to use multiple cores to calculate condition numbers (DEFAULT: FALSE)

fastStop

Halt early when the condition number changes by less than 1 for 3 iterations (DEFAULT: FALSE)

Value

A list with condition numbers and gene lists

Examples

library(ADAPTS)
LM22 <- ADAPTS::LM22
sigGenesList <- shrinkByKappa(sigMatrix=LM22[1:100,1:5], numChunks=4, 
verbose=FALSE, plotIt=FALSE, singleCore=TRUE, fastStop=TRUE)

Shrink a signature matrix

Description

Use shrinkByKappa and automatic minima detection to reduce a signature matrix. Select the new signature matrix with the minima and the maximum number of genes. There is an inherent difficult in that the condition number will tend to have a second peak at a relatively small number of genes, and then crash so that smallest condition number has more or less one gene.

By default, the algorithm will tend to pick the detected minima with the largest nubmer of genes. aggressiveMin=TRUE will try to find the minimum number of genes that has more genes than the maxima at the smallest number of genes

Usage

shrinkSigMatrix(
  sigMatrix,
  numChunks = 100,
  verbose = FALSE,
  plotIt = FALSE,
  aggressiveMin = TRUE,
  sigGenesList = NULL,
  singleCore = FALSE,
  fastStop = TRUE
)

Arguments

sigMatrix

The original signature matrix

numChunks

The number of groups of genes to remove. NULL is all genes (DEFAULT: 100)

verbose

Print out the current chunk as is it's being calculated (DEFAULT: NULL)

plotIt

Set to TRUE to plot (DEFAULT: FALSE)

aggressiveMin

Set to TRUE to aggresively seek the smallest number of genes (DEFAULT: TRUE)

sigGenesList

Set to use precomputed results from shrinkByKappa (DEFAULT: NULL)

singleCore

Set to FALSE to use multiple cores to calculate condition numbers (DEFAULT: FALSE)

fastStop

Halt early when the condition number changes by less than 1 for 3 iterations (DEFAULT: TRUE)

Value

A list with condition numbers and gene lists

Examples

library(ADAPTS)
LM22 <- ADAPTS::LM22
newSigMat <- shrinkSigMatrix(sigMatrix=LM22[1:100,1:5], numChunks=4, verbose=FALSE, 
plotIt=FALSE, aggressiveMin=TRUE, sigGenesList=NULL, singleCore=TRUE, fastStop=FALSE)

Spillover to convergence

Description

Build an n-pass spillover matrix, continuing until the results converge into clusters of cell types

deconMatrices <- spillToConvergence(sigMatrix, geneExpr, 100, FALSE, TRUE)

Usage

spillToConvergence(
  sigMatrix,
  geneExpr,
  nPasses = 100,
  plotIt = FALSE,
  imputNAs = FALSE,
  method = "DCQ"
)

Arguments

sigMatrix

The deconvolution matrix, e.g. LM22 or MGSM27

geneExpr

The source gene expression matrix used to calculate sigMatrix

nPasses

The maximum number of iterations (DEFAULT: 100)

plotIt

Set to TRUE to plot it (DEFAULT: FALSE)

imputNAs

Set to TRUE to imput genes with missing values & cache the imputed. FALSE will just remove them (DEFAULT: FALSE)

method

One of 'DCQ', 'SVMDECON', 'DeconRNASeq', 'proportionsInAdmixture', 'nnls' (DEFAULT: DCQ)

Value

A list of signature matrices

Examples

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

deconMatrices <- spillToConvergence(sigMatrix=smallLM22, geneExpr=fullLM22, nPasses=10, plotIt=TRUE)

Split a single cell dataset into multiple sets

Description

Take a matrix of single cell data with genes as rows and each column corresponding to a single cells. Break it up into rougly equal subsets, taking care to make sure that each cell type is represented in each set if possible

Usage

splitSCdata(
  RNAcounts,
  cellIDs = colnames(RNAcounts),
  numSets = 3,
  verbose = TRUE,
  randomize = TRUE
)

Arguments

RNAcounts

The single cell matrix

cellIDs

A vector will cell types for each column in scCountMatrix (DEFAULT: colnames(RNAcounts))

numSets

The number of sets to break it up into (DEFAULT: 3)

verbose

Set to TRUE to print cell counts as it goes (DEFAULT: TRUE)

randomize

Set to TRUE to randomize the sets (DEFAULT: TRUE)

Value

a list with a multiple sets

Examples

RNAcounts <- matrix(0, nrow=10, ncol=30)
rownames(RNAcounts) <- make.names(rep('Gene', nrow(RNAcounts)), unique=TRUE)
colnames(RNAcounts) <- make.names(c('CellX', rep('CellY', 9), 
rep('CellZ', 10), rep('CellB', 10)), unique=TRUE)
RNAcounts[, grepl('CellY', colnames(RNAcounts))] <- 1
RNAcounts[, grepl('CellZ', colnames(RNAcounts))] <- 2
RNAcounts[, grepl('CellB', colnames(RNAcounts))] <- 3
splitSCdata(RNAcounts, numSets=3)

Generate all the signature matrices one time with the option to leave out half of the data as a test set

Description

This wrapper is helpful for repetitively matrix generation. It generates seed matrix, all-gene matrix, augmented matrix, shrunk matrix, and all the clustered matrices in one call.

Usage

testAllSigMatrices(
  exprData,
  randomize = TRUE,
  skipShrink = FALSE,
  proportional = FALSE,
  handMetaCluster = NULL,
  testOnHalf = TRUE,
  condTol = 1.01,
  numChunks = 100,
  plotIt = TRUE,
  fastStop = TRUE,
  singleCore = TRUE
)

Arguments

exprData

The gene express data. Each row is a gene, and each column is an example of a particular cell type.

randomize

Set to to TRUE randomize the sets selected by ADAPTS::scSample (DEFAULT: TRUE)

skipShrink

Set to TRUE to skip shrinking the signatrure matrix (DEFAULT: TRUE)

proportional

Set to true to make the training set cell type proportional. Ignores group size (DEFAULT: FALSE)

handMetaCluster

A List of pre-defined meta clusters.Set to NULL to automatically group indistinguishable cells into same cluster using clustWspillOver.(DEFAULT: NULL)

testOnHalf

Set to TRUE to leave half the data as a test set

condTol

The tolerance in the reconstruction algorithm. 1.0 = no tolerance, 1.05 = 5% tolerance (DEFAULT: 1.01)

numChunks

The number of groups of genes to remove while shrinking (DEFAULT: NULL, i.e. 1)

plotIt

Set to FALSE to suppress plots (DEFAULT: TRUE)

fastStop

Halt early when the condition number changes by less than 1 for 3 iterations (DEFAULT: TRUE)

singleCore

TRUE for a single core (DEFAULT: TRUE)

Value

A list of results including prediction accuracy and cell enrichment

Examples

ct1 <- runif(1000, 0, 100)
ct2 <- runif(1000, 0, 100)
ct3 <- runif(1000, 0, 100)
ct4 <- runif(1000, 0, 100)
dataMat <- cbind(ct1, ct1, ct1, ct1, ct1, ct1, ct2, ct2, ct2, ct2, ct3, ct3, ct3,ct3,ct4,ct4)
rownames(dataMat) <- make.names(rep('gene', nrow(dataMat)), unique=TRUE)
noise <- matrix(runif(nrow(dataMat)*ncol(dataMat), -2, 2), nrow = nrow(dataMat), byrow = TRUE)
dataMat <- dataMat + noise
metaList <- list()
colnames(dataMat) <- sub('\\..*','', colnames(dataMat))
metaList[[1]] <- c(unique(colnames(dataMat))[1])  #Cell Type 1
metaList[[2]] <- c(unique(colnames(dataMat))[2])  #Cell Type 2
metaList[[3]] <- c(unique(colnames(dataMat))[3])  #Cell Type 3
metaList[[4]] <- c(unique(colnames(dataMat))[4:length(unique(colnames(dataMat)))])  #Cell Type 4
#options(mc.cores=2)
#  This is a meta-function that calls other functions, 
#  The execution speed is too slow for the CRAN automated check
#testAllSigMatrices(exprData=dataMat, randomize = TRUE, skipShrink=FALSE, 
#    proportional=FALSE, handMetaCluster=metaList, testOnHalf=TRUE, numChunks=NULL)

SVMDECONV helper function

Description

Use weightNorm to normalize the SVM weights. Used for SVMDECONV

w1 <- weightNorm(w)

Usage

weightNorm(w)

Arguments

w

The weight vector from fitting an SVM, something like something like t(fit1$coefs) %*% fit1$SV, where fit comes from <- svm(m~B, nu=0.25, kernel="linear"))

Value

a weight vector