SampleSort: Methods for ordering samples

In MCbiclust: Massive correlating biclusters for gene expression data and associated methods

Description

After finding an initial bicluster with FindSeed() the next step is to extend the bicluster by ordering the remaining samples by how they preserve the correlation found.

Usage

SampleSort(gem, seed, num.cores = 1, sort.length = NULL)

MultiSampleSortPrep(gem, av.corvec, top.genes.num, groups, initial.seeds)

Arguments

gem	Gene expression matrix with genes as rows and samples as columns
seed	Sample seed of highly correlating genes
num.cores	Number of cores used in parallel evaluation
sort.length	Number of samples to be sorted
av.corvec	List of average correlation vector
top.genes.num	Number of the top genes in correlation vector to use for sorting samples
groups	List showing what runs belong to which correlation vector group
initial.seeds	List of sample seeds from all runs

Details

SampleSort() is the basic function that achieves this, it takes the gene expression matrix, seed of samples, and also has options for the number of cores to run the method on and the number of samples to sort.

MultiSampleSortPrep() is a preparation function for SampleSort() when MCbiclust has been run multiple times and returns a list of gene expression matrices and seeds for each 'distinct' bicluster found.

Value

Order of samples by strength to correlation pattern

Examples

data(CCLE_small)
data(Mitochondrial_genes)

mito.loc <- (row.names(CCLE_small) %in% Mitochondrial_genes)
CCLE.mito <- CCLE_small[mito.loc,]

set.seed(102)
CCLE.seed <- FindSeed(gem = CCLE.mito,
                      seed.size = 10,
                      iterations = 100,
                      messages = 1000)

CCLE.sort <- SampleSort(gem = CCLE.mito,seed = CCLE.seed,sort.length = 11)

# Full ordering are in Vignette_sort in sysdata.rda
CCLE.samp.sort <- MCbiclust:::Vignette_sort[[1]]

CCLE.pc1 <- PC1VecFun(top.gem = CCLE.mito,
                      seed.sort = CCLE.samp.sort,
                      n = 10)

CCLE.cor.vec <-  CVEval(gem.part = CCLE.mito,
                            gem.all = CCLE_small,
                            seed = CCLE.seed,
                            splits = 10)

CCLE.bic <- ThresholdBic(cor.vec = CCLE.cor.vec,sort.order = CCLE.samp.sort,
                         pc1 = as.numeric(CCLE.pc1))

CCLE.pc1 <- PC1Align(gem = CCLE_small, pc1 = CCLE.pc1,
                     cor.vec = CCLE.cor.vec ,
                     sort.order = CCLE.samp.sort,
                     bic =CCLE.bic)

CCLE.fork <- ForkClassifier(CCLE.pc1, samp.num = length(CCLE.bic[[2]]))

MCbiclust documentation built on Nov. 8, 2020, 11:09 p.m.