newTranslatomeDataset {tRanslatome} | R Documentation

## newTranslatomeDatasetHelpfile

### Description

This function takes as input a normalized data matrix coming from an high-throughput experiment, along with vectors of column names (or numeric vectors of columns) defining the samples for each of the conditions. It takes as an input a character label specifying the data type(array, ngs) and returns an object of the class `TranslatomeDataset`, which stores the matrix and the samples definitions and allow to call the `computeDEGs` function on it to compute differentially expressed genes in the various conditions.

### Usage

    newTranslatomeDataset(expr.matrix, cond.a, cond.b, cond.c, cond.d,
        data.type="array", label.level=c("1st level","2nd level"),
        label.condition=c("control","treated"))

### Arguments

`expr.matrix` | a matrix that contains the normalized signal intensity data, each row representing a gene and each column representing a sample. Row names should correspond to gene names, column names should correspond to sample names,
`cond.a` | a character vector of column names (or a numeric vector of columns) belonging to expr.matrix. These columns contain the signal intensity data coming from the samples of the first level of the control condition (in our example: total RNA, undifferentiated cells),
`cond.b` | a character vector of column names (or a numeric vector of columns) belonging to expr.matrix. These columns contain the signal intensity data coming from the samples of the first level of the treatment condition(in our example: total RNA, differentiated cells),
`cond.c` | a character vector of column names (or a numeric vector of columns) belonging to expr.matrix. These columns contain the signal intensity data coming from the samples of the second level of the control condition (in our example: polysomal RNA, undifferentiated cells),
`cond.d` | a character vector of column names (or a numeric vector of columns) belonging to expr.matrix. These columns contain the signal intensity data coming from the samples of the second level of the treatment group (in our example: polysomal RNA, differentiated cells),
`data.type` | a character specifying the type of data represented by `expr.matrix`. By default it is set to `array`, the other accepted value is `ngs`,
`label.level` | a character vector specifying the names given to the two levels. By default the vector is set to `c("1st level","2nd level")`, but the user can specify other names: in our example the two levels are named "transcriptome" and "translatome",
`label.condition` | a character vector specifying the names given to the two conditions. By default, the vector is set to `c("control","treated")`, but the user can specify other names: in our example the two levels are named "undifferentiated" and "differentiated",

### Details

Signals contained in expr.matrix should be previously normalized with standard methods (quantile, percentile shift, ... ) when data is coming from microarrays or in the appropriate cases when it is coming from sequencing experiments.

### Value

An object of class `TranslatomeDataset`

### Author(s)

Toma Tebaldi, Erik Dassi, Galena Kostoska

### See Also

`TranslatomeDataset` `computeDEGs` `DEGs` `Scatterplot` `Histogram` `CVplot` `MAplot` `SDplot`

### Examples

    data(tRanslatomeSampleData)
    translatome.analysis <-
        newTranslatomeDataset(expressionMatrix,
            c("tot.undiff.a","tot.undiff.b","tot.undiff.c"),
            c("tot.diff.a","tot.diff.b","tot.diff.c"),
            c("pol.undiff.a","pol.undiff.b","pol.undiff.c"),
            c("pol.diff.a","pol.diff.b","pol.diff.c"),
            data.type="array", label.level=c("transcriptome","translatome"),
            label.condition=c("undifferentiated","differentiated"))

[Package tRanslatome version 1.30.0 Index]