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

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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

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# 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)

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