R: Microarray Time Series Data for T-Cell Activation

tcell {longitudinal} | R Documentation

## Microarray Time Series Data for T-Cell Activation

### Description

The data result from two experiments investigating the expression response of human T cells to PMA and ionomicin treatment.

The first data set (`tcell.34`) contains the temporal expression levels of 58 genes for 10 unequally spaced time points. At each time point there are 34 separate measurements. The second data set (`tcell.10`) stems from a related experiment considering the same genes and identical time points, and contains 10 further measurements per time point. See Rangel et al. (2004) for more details.

### Usage

    data(tcell)

### Format

`tcell.10` and `tcell.34` are `longitudinal` objects, i.e. matrices with 58 colums each and a number of extra attributes (see `longitudinal` and `longitudinal.util`).

The vector `tcell.gene.descriptions` contains the description of the functions of the 58 investigated genes.

### Source

This data is described in Rangel et al. (2004).

### References

Rangel, C., Angus, J., Ghahramani, Z., Lioumi, M., Sotheran, E., Gaiba, A., Wild, D. L., and Falciani, F. (2004) Modeling T-cell activation using gene expression profiling and state-space models. Bioinformatics, 20, 1361–1372.

### Examples

    # load "longitudinal" library
    library("longitudinal")

    # load data sets
    data(tcell)

    # data set with 10 repeats
    dim(tcell.10)
    summary(tcell.10)
    is.longitudinal(tcell.10)
    is.regularly.sampled(tcell.10)
    is.equally.spaced(tcell.10)
    get.time.repeats(tcell.10)

    # data set with 34 repeats
    dim(tcell.34)
    summary(tcell.34)
    is.longitudinal(tcell.34)
    is.regularly.sampled(tcell.34)
    is.equally.spaced(tcell.34)
    get.time.repeats(tcell.34)

    # descriptions of the first nine genes
    tcell.gene.description[1:9]

    # plot the first nine time series
    plot(tcell.10, 1:9)
    plot(tcell.34, 1:9)

    # Rangel et al. use the combined data set
    tcell.44 <- combine.longitudinal(tcell.34, tcell.10)
    plot(tcell.44, 1:9)

[Package longitudinal version 1.1.12 Index]