ttmap_sgn_genes: Gives a list of associated genes per cluster

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

Description

ttmap_sgn_genes function

Usage

   
    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)

Arguments

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

Details

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

Value

generates a file per cluster of significant features with an annotation

Author(s)

Rachel Jeitziner

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

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