make_matrices {TTMap} | R Documentation

## Prepares the matrices for `control_adjustment`

### 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))

[Package TTMap version 1.12.0 Index]