make_matrices: Prepares the matrices for 'control_adjustment'

Description Usage Arguments Details Value Author(s) See Also Examples

Description

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

Usage

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    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

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    ##--
    ##--
    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.