Follows the official maSigPro sequence: make.design.matrix(), p.vector(),
T.fit(), and get.siggenes().
Usage
de_masigpro(
x,
experiment,
edesign,
assay,
degree = 2,
q = 0.05,
p_adjust = "BH",
min_observations = NULL,
counts = FALSE,
step_method = "backward",
alpha = 0.05,
r_squared = 0.6,
variables = "groups",
...
)Arguments
- x
A
MultiAssayExperimentobject.- experiment
Optional experiment name.
- edesign
Experimental design data frame required by maSigPro. Rows must be row-named by assay sample ID and include
Time,Replicates, and at least one binary group-indicator column. Rows are reordered to assay sample order before fitting.- assay
Assay name or one-based assay index.
- degree
Polynomial degree for the regression model.
- q, p_adjust
Significance threshold and adjustment method for
maSigPro::p.vector().- min_observations
Optional minimum observations per gene.
- counts
Whether the assay contains counts.
- step_method
Variable-selection method used by
maSigPro::T.fit().- alpha
Significance threshold for variable selection.
- r_squared
Minimum model R-squared.
- variables
maSigPro variable grouping passed as
vars.- ...
Additional arguments passed to
maSigPro::p.vector().