AugmentSigMatrix {ADAPTS}R Documentation

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()
)

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())

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)

[Package ADAPTS version 1.0.6 Index]