ttmap: Visualisation of the clustering

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

Description

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

Usage

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    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

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    ##--
    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.