projectR-methods: Generic projectR function

Description Usage Arguments Details Value Examples

Description

A function for the projection of new data into a previously defined feature space.

Usage

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projectR(data, loadings, dataNames = NULL, loadingsNames = NULL, ...)

## S4 method for signature 'matrix,matrix'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  family = "gaussianff",
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,LinearEmbeddingMatrix'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  model = NA,
  family = "gaussianff",
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,prcomp'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE
)

## S4 method for signature 'matrix,rotatoR'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE
)

## S4 method for signature 'matrix,correlateR'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,hclust'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  full = FALSE,
  targetNumPatterns,
  sourceData,
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,kmeans'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  full = FALSE,
  sourceData,
  bootstrapPval = FALSE,
  bootIter = 1000
)

Arguments

data

Target dataset into which you will project. It must of type matrix.

loadings

loadings learned from source dataset.

dataNames

a vector containing unique name, i.e. gene names, for the rows of the target dataset to be used to match features with the loadings, if not provided by rownames(data). Order of names in vector must match order of rows in data.

loadingsNames

a vector containing unique names, i.e. gene names, for the rows ofloadings to be used to match features with the data, if not provided by rownames(loadings). Order of names in vector must match order of rows in loadings.

...

Additional arguments to projectR

NP

vector of integers indicating which columns of loadings object to use. The default of NP=NA will use entire matrix.

full

logical indicating whether to return the full model solution. By default only the new pattern object is returned.

family

VGAM family function for model fitting (default: "gaussianff")

bootstrapPval

logical to indicate whether to generate p-values using bootstrap, not available for prcomp and rotatoR objects

bootIter

number of bootstrap iterations, default = 1000

model

Optional arguements to choose method for projection

targetNumPatterns

desired number of patterns with hclust

sourceData

data used to create cluster object

Details

loadings can belong to one of several classes depending on upstream analysis. Currently permitted classes are matrix, CogapsResult, CoGAPS, pclust, prcomp, rotatoR, and correlateR.

Value

A matrix of sample weights for each input basis in the loadings matrix (if full=TRUE, full model solution is returned).

Examples

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projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=AP.RNAseq6l3c3t$Amean, 
dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

library("CoGAPS")
CR.RNAseq6l3c3t <- CoGAPS(p.RNAseq6l3c3t, params = new("CogapsParams",
nPatterns=5))
projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=CR.RNAseq6l3c3t,
dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

pca.RNAseq6l3c3t<-prcomp(t(p.RNAseq6l3c3t))
pca.ESepiGen4c1l<-projectR(data=p.ESepiGen4c1l$mRNA.Seq, 
loadings=pca.RNAseq6l3c3t, dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

pca.RNAseq6l3c3t<-prcomp(t(p.RNAseq6l3c3t))
r.RNAseq6l3c3t<-rotatoR(1,1,-1,-1,pca.RNAseq6l3c3t$rotation[,1:2])
pca.ESepiGen4c1l<-projectR(data=p.ESepiGen4c1l$mRNA.Seq, 
loadings=r.RNAseq6l3c3t, dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

c.RNAseq6l3c3t<-correlateR(genes="T", dat=p.RNAseq6l3c3t, threshtype="N", 
threshold=10, absR=TRUE)
cor.ESepiGen4c1l<-projectR(data=p.ESepiGen4c1l$mRNA.Seq, loadings=c.RNAseq6l3c3t, 
NP="PositiveCOR", dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

projectR documentation built on Nov. 8, 2020, 7:50 p.m.