ttmap {TTMap} | R Documentation |
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 |
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)