generate_correlation {TTMap} | R Documentation |
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.
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)
ttmap_part1_hda |
an object given back
by |
select |
A sublist of rownames of ttmap_part1_hda$Dc.Dmat |
alpha |
A real number corresponding to a cutoff |
If one is interested only in clustering samples according to a list of genes belonging to a certain pathway, then this list is provided to the parameter select. Alpha is a cutoff for deviations that should be considered as noise, for gene expression data such as normalised RNA-seq or microarrays for instance a cutoff of 1, corresponding to a two fold change is being chosen.
Distance matrix
Rachel Jeitziner
ttmap_part1_hda <- list() ttmap_part1_hda$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))