ttmap {TTMap} | R Documentation

## Visualisation of the clustering

### 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)

[Package TTMap version 1.10.0 Index]