ttmap: Visualisation of the clustering
In TTMap: Two-Tier Mapper: a clustering tool based on topological data analysis

Description Usage Arguments Details Value Author(s) See Also Examples

Enables a quick view on the groups in the dataset (globally) and how locally they differ.

    ttmap(ttmap_part1_hda, m1, 
    select = row.names(ttmap_part1_hda$Dc.Dmat), 
    ddd, e, filename = "TEST", n = 3, ad = 0, bd = 0, piq = 1, 
    dd = generate_mismatch_distance(ttmap_part1_hda = ttmap_part1_hda, 
    select = select), mean_value_m1 = "N", ni = 2)

`ttmap_part1_hda`	list output of `hyperrectangle_deviation_assessment`
`m1`	either a user imputed vector whose names are the names of the samples with addition of .Dis. or by default it is the amount of deviation
`select`	Should all the features (default) or only a sublist be considered to calculate the distance
`ddd`	Annotation matrix with rownames the different sample names with addition of .Dis. There can be as many columns as wanted, but only the column n will be selected to annotated the clusters
`e`	integer parameter defining under which value two samples are considered to be close
`filename`	Name for the description file annotating the clusters
`n`	The column to be considered to annotate the clusters
`ad`	if ad!=0 then the clusters on the output picture will not be annotated
`bd`	if different than 0 (default), the output will be without outliers of the test data set (clusters composed of only "piq" element)
`piq`	parameter used to determine what small clusters are, see bd
`dd`	the distance matrix to be used
`mean_value_m1`	if == "N" the average of the values in m1 divided by the number of the samples are put into the legend (by default represents the average of the samples in a cluster of the mean-deviation of the features) otherwise it will show the average value of the values in m1 (is useful for instance if m1 represents the age of the samples)
`ni`	The column to consider to annotate the samples (is put into parenthesis) for the description file

Is the Two-tiers Mapper function. The output is an interactive image of the clusters in the different layers.

`all`	the clusters in the overall group
`low`	the clusters in the lower quartile group
`mid1`	the clusters in the first middle quartile group
`mid2`	the clusters in the second middle quartile group
`high`	the clusters in the higher quartile group

Rachel Jeitziner

control_adjustment, hyperrectangle_deviation_assessment, ttmap_sgn_genes

    ##--
    library(airway)
    data(airway)
    airway <- airway[rowSums(assay(airway))>80,]
    assay(airway) <- log(assay(airway)+1,2)
    ALPHA <- 1
    the_experiment <- TTMap::make_matrices(airway,
    seq_len(4), seq_len(4) + 4,
    rownames(airway), rownames(airway))
    TTMAP_part1prime <-TTMap::control_adjustment(
    normal.pcl = the_experiment$CTRL,
    tumor.pcl = the_experiment$TEST, 
    normalname = "The_healthy_controls", 
    dataname = "Effect_of_cancer", 
    org.directory = tempdir(), e = 0, P = 1.1, B = 0);
    Kprime <- 4;
    TTMAP_part1_hda <-
    TTMap::hyperrectangle_deviation_assessment(x = 
    TTMAP_part1prime,
    k = Kprime,dataname = "Effect_of_cancer",
    normalname = "The_healthy_controls");
    annot <- c(paste(colnames(
    the_experiment$TEST[,-(seq_len(3))]),"Dis", sep = "."),
    paste(colnames(the_experiment$CTRL[, 
    -seq_len(3)]), "Dis", sep = "."))
    annot <- cbind(annot, annot)
    rownames(annot)<-annot[, 1]
    dd5_sgn_only <-TTMap::generate_mismatch_distance(
    TTMAP_part1_hda,
    select=rownames(TTMAP_part1_hda$Dc.Dmat), alpha = ALPHA)
    TTMAP_part2 <-
    TTMap::ttmap(TTMAP_part1_hda, TTMAP_part1_hda$m,
    select = rownames(TTMAP_part1_hda$Dc.Dmat), annot,
    e = TTMap::calcul_e(dd5_sgn_only, 0.95, TTMAP_part1prime, 1), 
    filename = "first_comparison", n =  1, dd = dd5_sgn_only)