Extract significant genes based on R-Square and P-values

`sc.filter()` creates lists of significant genes based on user-specified constraints.

sc.filter(
  scmpObj,
  rsq = 0.7,
  p_value = scmpObj@Parameters@p_value,
  vars = c("all", "each", "groups"),
  intercept = "dummy",
  term_p_value = 0.05,
  includeInflu = TRUE
)

Arguments

scmpObj: An object of class ScMaSigPro.
rsq: Coefficient of determination or R-squared value threshold.
p_value: Overall model significance.
vars: Variables for which to extract significant genes. See details.
intercept: Specify the branching path treated as reference. See details. (When `vars` equals "groups").
term_p_value: Term wise significance.
includeInflu: Whether to include genes with influential observations.

Value

An object of class ScMaSigPro, with updated `Significant` slot.

Details

`vars` Parameter can take one of the following values:

"all": Generates one gene list with all significant genes.
"each": Generates gene list for each term in the polynomial GLM.
"groups": Generates gene list for each branching path.

`intercept` Parameter modulates the treatment for intercept coefficients to apply for selecting significant genes when `vars` equals "groups". There are three possible values:

"none": No significant intercept (differences) are considered.
"dummy": Includes genes with significant intercept differences between branching paths.
"all": When both significant intercept coefficient for the reference path and significant intercept differences are considered for selecting significant genes.

References

Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102

Author