generate_correlation: Generates different distance matrices

In jeitziner/TTMap: Two-Tier Mapper: a clustering tool based on topological data analysis

Description

Single cell complete mismatch distance, single cell complete mismatch distance with a parameter of cutoff, mismatch distance, correlation distance, p-value of correlation test distance and euclidean distance.

Usage

    generate_single_cell_complete_mismatch(ttmap_part1_hda, 
    select, alpha = 1)
    generate_single_cell_mismatch_with_parameter(ttmap_part1_hda,
    select, alpha = 1)
    generate_correlation(ttmap_part1_hda, select)
    generate_euclidean(ttmap_part1_hda, select)
    generate_mismatch_distance(ttmap_part1_hda, select, alpha = 1)
    generate_p_val_correlation(ttmap_part1_hda, select)

Arguments

ttmap_part1_hda	an object given back by hyperrectangle_deviation_assessment
select	A sublist of rownames of ttmap_part1_hda$Dc.Dmat
alpha	A real number corresponding to a cutoff

Details

If one is interested only in clustering samples according to a list of genes belonging to a certain pathway, then this list is provided to the parameter select. Alpha is a cutoff for deviations that should be considered as noise, for gene expression data such as normalised RNA-seq or microarrays for instance a cutoff of 1, corresponding to a two fold change is being chosen.

Value

Distance matrix

Author(s)

Rachel Jeitziner

Examples

    ttmap_part1_hda <- list()
    ttmap_part1_hda$Dc.Dmat <- matrix(c(-1, 2, 0, -4, 5, 6), nrow = 2)
    rownames(ttmap_part1_hda$Dc.Dmat) <- c("Gene1", "Gene2")
    colnames(ttmap_part1_hda$Dc.Dmat) <- c("A", "B", "C")
    dd <- TTMap::generate_mismatch_distance(ttmap_part1_hda, select = 
    rownames(ttmap_part1_hda$Dc.Dmat))
    dd <- TTMap::generate_euclidean(ttmap_part1_hda, select = 
    rownames(ttmap_part1_hda$Dc.Dmat))

jeitziner/TTMap documentation built on May 23, 2019, 4:24 p.m.