SampleSort: Methods for ordering samples

Description Usage Arguments Details Value Examples

View source: R/SampleSort.R

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

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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

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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.