evalDiffCorr-methods: Evaluate Differential Correlation

In GabrielHoffman/decorate: Differential Epigenetic Coregulation Test

Evaluate Differential Correlation

Description

Evaluate Differential Correlation between two subsets of data

Usage

evalDiffCorr(
  epiSignal,
  testVariable,
  gRanges,
  clustList,
  npermute = c(100, 10000, 1e+05),
  adj.beta = 0,
  rho = 0,
  sumabs.seq = 1,
  BPPARAM = bpparam(),
  method = c("sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich",
    "Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U",
    "WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"),
  method.corr = c("pearson", "kendall", "spearman")
)

## S4 method for signature 'EList,ANY,GRanges,list'
evalDiffCorr(
  epiSignal,
  testVariable,
  gRanges,
  clustList,
  npermute = c(100, 10000, 1e+05),
  adj.beta = 0,
  rho = 0,
  sumabs.seq = 1,
  BPPARAM = bpparam(),
  method = c("sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich",
    "Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U",
    "WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"),
  method.corr = c("pearson", "kendall", "spearman")
)

## S4 method for signature 'matrix,ANY,GRanges,list'
evalDiffCorr(
  epiSignal,
  testVariable,
  gRanges,
  clustList,
  npermute = c(100, 10000, 1e+05),
  adj.beta = 0,
  rho = 0,
  sumabs.seq = 1,
  BPPARAM = bpparam(),
  method = c("sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich",
    "Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U",
    "WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"),
  method.corr = c("pearson", "kendall", "spearman")
)

## S4 method for signature 'data.frame,ANY,GRanges,list'
evalDiffCorr(
  epiSignal,
  testVariable,
  gRanges,
  clustList,
  npermute = c(100, 10000, 1e+05),
  adj.beta = 0,
  rho = 0,
  sumabs.seq = 1,
  BPPARAM = bpparam(),
  method = c("sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich",
    "Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U",
    "WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"),
  method.corr = c("pearson", "kendall", "spearman")
)

Arguments

epiSignal	matrix or EList of epigentic signal. Rows are features and columns are samples
testVariable	factor indicating two subsets of the samples to compare
gRanges	GenomciRanges corresponding to the rows of epiSignal
clustList	list of cluster assignments
npermute	array of two entries with min and max number of permutations
adj.beta	parameter for sLED
rho	a large positive constant such that A(X)-A(Y)+diag(rep(rho,p)) is positive definite. Where p is the number of features
sumabs.seq	sparsity parameter
BPPARAM	parameters for parallel evaluation
method	"sLED", "Box", "Box.permute", "Steiger.fisher", "Steiger", "Jennrich", "Factor", "Mann.Whitney", "Kruskal.Wallis", "Cai.max", "Chang.maxBoot", "LC.U", "WL.randProj", "Schott.Frob", "Delaneau", "deltaSLE"
method.corr	Specify type of correlation: "pearson", "kendall", "spearman"

Details

Correlation sturucture between two subsets of the data is evaluated with sparse-Leading-Eigenvalue-Driven (sLED) test:

Zhu, Lingxue, Jing Lei, Bernie Devlin, and Kathryn Roeder. 2017. Testing high-dimensional covariance matrices, with application to detecting schizophrenia risk genes. Annals of Applied Statistics. 11:3 1810-1831. doi:10.1214/17-AOAS1062

Value

list of result by chromosome and clustList

Examples

library(GenomicRanges)
library(EnsDb.Hsapiens.v86)

# load data
data('decorateData')

# load gene locations
ensdb = EnsDb.Hsapiens.v86

# Evaluate hierarchical clsutering
treeList = runOrderedClusteringGenome( simData, simLocation ) 

# Choose cutoffs and return clusters
treeListClusters = createClusters( treeList, method = "meanClusterSize", meanClusterSize=c( 10, 20) )

# Plot correlations and clusters in region defined by query
query = range(simLocation)

# Plot clusters
plotDecorate( ensdb, treeList, treeListClusters, simLocation, query)

# Evaluate Differential Correlation between two subsets of data
sledRes = evalDiffCorr( simData, metadata$Disease, simLocation, treeListClusters, npermute=c(20, 200, 2000))

# get summary of results
df = summary( sledRes )

# print results
head(df)

# extract peak ID's from most significant cluster
peakIDs = getFeaturesInCluster( treeListClusters, df$chrom[1], df$cluster[1], "20")

# plot comparison of correlation matrices for peaks in peakIDs
#  where data is subset by metadata$Disease
main = paste0(df$chrom[1], ': cluster ', df$cluster[1])
plotCompareCorr( simData, peakIDs, metadata$Disease) + ggtitle(main)

GabrielHoffman/decorate documentation built on May 23, 2023, 1:29 a.m.