ttmap: Visualisation of the clustering

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

Description

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

Usage

    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)

Arguments

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

Details

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

Value

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

Author(s)

Rachel Jeitziner

See Also

control_adjustment, hyperrectangle_deviation_assessment, ttmap_sgn_genes

Examples

    ##--
    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)

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