build_study {edge} | R Documentation

## Formulates the experimental models

### Description

`build_study` generates the full and null models for users unfamiliar with building models in R. There are two types of experimental designs: static and time-course. For more details, refer to the vignette.

### Usage

    build_study(data, grp = NULL, adj.var = NULL, bio.var = NULL,
      tme = NULL, ind = NULL, sampling = c("static", "timecourse"),
      basis.df = 2, basis.type = c("ncs", "poly"))

### Arguments

`data` | `matrix`: gene expression data (rows are genes, columns are samples).
`grp` | `vector`: group assignement in the study (for K-class studies). Optional.
`adj.var` | `matrix`: adjustment variables. Optional.
`bio.var` | `matrix`: biological variables. Optional.
`tme` | `vector`: time variable in a time course study. Optional.
`ind` | `factor`: individual factor for repeated observations of the same individuals. Optional.
`sampling` | `string`: type of study. Either "static" or "timecourse". Default is "static".
`basis.df` | `numeric`: degrees of freedom of the basis for time course study. Default is 2.
`basis.type` | `string`: either "ncs" (natural cubic spline) or "ps" (polynomial spline) basis for time course study. Default is "ncs".

### Value

`deSet` object

### Author(s)

John Storey, Andy Bass

### See Also

`deSet`, `build_models`

### Examples

    # create ExpressionSet object from kidney dataset
    library(splines)
    data(kidney)
    age <- kidney$age
    sex <- kidney$sex
    kidexpr <- kidney$kidexpr

    # create deSet object from data
    de_obj <- build_study(data = kidexpr, adj.var = sex, tme = age,
    sampling = "timecourse", basis.df = 4)

[Package edge version 2.19.0 Index]