make_matrices: Prepares the matrices for 'control_adjustment'

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

Description

make_matrices generates the control and the test matrice in the right format

Usage

    make_matrices(mat, col_ctrl, col_test, NAME, CLID,
    GWEIGHT = rep(1, dim(mat)[1]), EWEIGHT = 0)

Arguments

mat	the gene expressions can be matrix, data.frame, " RangedSummarizedExperiment", " ExpressionSet" format
col_ctrl	the columns in the matrix "mat" of the control samples
col_test	the columns in the matrix "mat" of the test samples
NAME	Name of genes,or annotation, e.g. WNT4
CLID	Identities of genes,e.g. ENSMUSG00000000001
GWEIGHT	the weight for each gene
EWEIGHT	the weight for each experiment

Details

make_matrices generates the test matrix and the control matrix in the format accepted by control_adjustment from a matrix object

Value

junk	A list containing $CTRL and $TEST the matrices to impute in control_adjustment

Author(s)

Rachel Jeitziner

See Also

control_adjustment, hyperrectangle_deviation_assessment, ttmap_sgn_genes, " RangedSummarizedExperiment"

Examples

    ##--
    ##--
    Aa = 6
    B1 = 3
    B2 = 3
    C0 = 100
    D0 = 10000
    a0 = 4
    b0 = 0.1
    a1 = 6
    b1 = 0.1
    a2 = 2
    b2 = 0.5
    ALPHA = 1
    E = 1
    Pw = 1.1
    Bw = 0
    RA <- matrix(rep(0, Aa * D0), nrow = D0)
    RB1 <- matrix(rep(0, B1 * D0), nrow = D0)
    RB2 <- matrix(rep(0, B2 * D0), nrow = D0)
    RA <- lapply(seq_len(D0 - C0), function(i) rnorm(Aa, 
    mean = a0, sd = sqrt(b0)))
    RA<-do.call(rbind, RA)
    RB1<- lapply(seq_len(D0 - C0), function(i) rnorm(B1, 
    mean = a0, sd = sqrt(b0)))
    RB1 <- do.call(rbind, RB1)
    RB2 <- lapply(seq_len(D0 - C0), function(i) rnorm(B2, 
    mean = a0, sd = sqrt(b0)))
    RB2 <- do.call(rbind, RB2)
    RA_c <- lapply(seq_len(C0), function(i) rnorm(Aa, 
    mean = a0, sd = sqrt(b0)))
    RA_c <- do.call(rbind, RA_c)
    RB1_c <- lapply(seq_len(C0), function(i) rnorm(B1, 
    mean = a1, sd = sqrt(b1)))
    RB1_c <- do.call(rbind, RB1_c)
    RB2_c <- lapply(seq_len(C0), function(i) rnorm(B2, 
    mean = a2, sd = sqrt(b2)))
    RB2_c <- do.call(rbind, RB2_c)
    norm1 <- rbind(RA, RA_c)
    dis <- cbind(rbind(RB1, RB1_c), rbind(RB2, RB2_c))
    colnames(norm1) <- paste("N", seq_len(Aa), sep = "")
    rownames(norm1) <- c(paste("norm", seq_len(D0 - C0), sep = ""),
    paste("diff", seq_len(C0), sep = ""))
    colnames(dis) <- c(paste("B1", seq_len(B1), sep=""),
    paste("B2", seq_len(B2), sep  =""))
    rownames(dis)<-c(paste("norm",
    seq_len(D0 - C0), sep = ""), 
    paste("diff", seq_len(C0), sep = ""))
    the_experiment <- TTMap::make_matrices(cbind(norm1, dis),
    col_ctrl = colnames(norm1),
    col_test = colnames(dis), NAME = rownames(norm1),
    CLID = rownames(norm1))
    ###other example using SummarizedExperiment
    library(airway)
    data(airway)
    airway <- airway[rowSums(assay(airway))>80,]
    assay(airway) <- log(assay(airway)+1,2)
    the_experiment <- TTMap::make_matrices(airway, 
    seq_len(4), seq_len(4) + 4,
    rownames(airway), rownames(airway))

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