ttmap_sgn_genes: Gives a list of associated genes per cluster
In TTMap: Two-Tier Mapper: a clustering tool based on topological data analysis

Description Usage Arguments Details Value Author(s) Examples

ttmap_sgn_genes function

   
    ttmap_sgn_genes(ttmap_part2_gtlmap, ttmap_part1_hda,
    ttmap_part1_ctrl_adj, c, n = 2, a = 0, 
    filename = "TEST2", annot = ttmap_part1_ctrl_adj$tag.pcl, 
    col = "NAME", path = getwd(), Relaxed = 1)
    ttmap_sgn_genes_inter2(q, ttmap_part1_hda, alpha = 0)
    ttmap_sgn_genes_inter(q, ttmap_part1_hda, alpha = 0)

`ttmap_part2_gtlmap`	output of `ttmap`
`ttmap_part1_hda`	output of `hyperrectangle_deviation_assessment`
`ttmap_part1_ctrl_adj`	output of `control_adjustment`
`c`	annotation file of the samples
`n`	column to give the name to the cluster
`a`	cutoff to be considered different than noise
`filename`	Name of the files
`annot`	annotation file
`col`	which column should be considered to annotate the features
`path`	where to put the output files
`Relaxed`	If Relaxed then one allows sample to be as the control and for all the others in one cluster to be going in the same direction (more than alpha) otherwise all the features must be deviating to be considered a significant feature
`q`	The sample in one cluster
`alpha`	cutoff to be considered different than noise inherited by a

Is giving per cluster the features that vary in the same direction

generates a file per cluster of significant features with an annotation

Rachel Jeitziner

    ##--
    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$Dc.Dmat), annot,
    e = TTMap::calcul_e(dd5_sgn_only, 0.95, TTMAP_part1prime, 1), 
    filename = "first_comparison", n =  1, dd = dd5_sgn_only)
    TTMap::ttmap_sgn_genes(TTMAP_part2, TTMAP_part1_hda, 
    TTMAP_part1prime, annot,
    n = 2, a = 1, filename = "first_list_of_genes",
    annot = TTMAP_part1prime$tag.pcl, col = "NAME", 
    path = getwd(), Relaxed = 1)