generate_correlation {TTMap} | R Documentation

## Generates different distance matrices

### 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))

[Package TTMap version 1.10.0 Index]