build_study: Formulates the experimental models
In jdstorey/edge: Extraction of Differential Gene Expression

Description Usage Arguments Value Author(s) See Also Examples

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.

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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"))

`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".

deSet object

John Storey, Andy Bass

deSet, build_models

# 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)