Performs a regression fit for each gene taking all variables present in the model.
sc.p.vector(
scmpObj,
p_value = 0.05,
mt_correction = "BH",
min_na = 6,
family = negative.binomial(theta = 10),
epsilon = 1e-08,
verbose = TRUE,
offset = TRUE,
parallel = FALSE,
n_cores = availableCores() - 2,
log_offset = FALSE,
max_it = 100,
link = "log"
)An object of class ScMaSigPro.
Significance level used for variable selection in the stepwise regression.
A character string specifying the p-value correction method.
Minimum values needed per gene across cells to estimate the model.
Distribution of the error term.
Model convergence tolerance.
Print detailed output in the console. (Default is TRUE)
logical value specifying whether to use offset during fitting.
Use forking process to run parallelly. (Default is FALSE) (Currently, Windows is not supported)
Explicitly specify the number of cores to use for parallel model fitting. (Default is inferred from the system using availableCores()-2)
A logical value specifying whether to take the logarithm of the offsets.
Maximum number of iterations to fit the model.
Type of link function to use in the model. Default is "log".
An object of class ScMaSigPro, with updated `Profile`
slot.
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