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Select the significant terms of each gene

Source: R/sc.t.fit.R
sc.t.fit.Rd

sc.t.fit() selects by stepwise regression, as maSigPro does, the significant terms of the full model of each gene with a non-flat profile (sc.p.vector()), and keeps the coefficients, p-values and t statistics of the selected models.

Usage

sc.t.fit(
  scmpObj,
  selection_method = "backward",
  p_value = scmpObj@Parameters@p_value,
  nvar_correction = FALSE,
  family = scmpObj@Parameters@distribution,
  epsilon = scmpObj@Parameters@epsilon,
  offset = scmpObj@Parameters@offset,
  verbose = TRUE,
  max_it = scmpObj@Parameters@max_it,
  link = scmpObj@Parameters@link,
  BPPARAM = SerialParam()
)

Arguments

scmpObj

A ScMaSigPro object.

selection_method

The stepwise method: "backward", "forward", "two.ways.backward" or "two.ways.forward".

p_value

The level at which a term enters or stays in a model; by default that of sc.p.vector().

nvar_correction

Whether to divide p_value by the number of terms of the design.

family

The distribution of the error term, a stats::family() object; by default the family of sc.p.vector(), with its link. When sc.p.vector() estimated one theta per gene (theta_estimate = "gene"), each gene is fitted with its own theta.

epsilon

The convergence tolerance of stats::glm().

offset

Whether to add the offset of each bin, the logarithm of its size factor, which needs the log link. The fits use the offsets that sc.p.vector() stored in the Design slot; when it stored none (offset = FALSE), they are computed here in the same way.

verbose

Whether to print progress messages.

max_it

The maximum number of iterations of stats::glm().

The link of family, by default that of sc.p.vector(); when given, it must be the link of family. Choose the link in sc.p.vector().

BPPARAM

A BiocParallelParam object that runs the per-gene fits, such as BiocParallel::MulticoreParam(2) (Linux and macOS) or BiocParallel::SnowParam(2) (any system). The default, BiocParallel::SerialParam(), fits the genes one after another; create it with progressbar = TRUE for a progress bar. The results do not depend on the backend.

Value

The object with the selected model of each gene with a non-flat profile in the Estimate slot (class Estimates), read by showSol() and the other result readers.

References

Conesa A, Nueda MJ, Ferrer A, Talón M (2006). maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments. Bioinformatics 22(9), 1096-1102. doi:10.1093/bioinformatics/btl056

Nueda MJ, Tarazona S, Conesa A (2014). Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics 30(18), 2598-2602. doi:10.1093/bioinformatics/btu333

Srivastava P, Benegas Coll M, Götz S, Nueda MJ, Conesa A (2024). scMaSigPro: differential expression analysis along single-cell trajectories. Bioinformatics 40(7), btae443. doi:10.1093/bioinformatics/btae443

See also

showSol() and the other readers of the results; Estimates.

Other workflow steps: sc.cluster.trend(), sc.filter(), sc.p.vector(), sc.restruct(), sc.set.poly(), sc.squeeze()

Author

Priyansh Srivastava spriyansh29@gmail.com, Ana Conesa and Maria Jose Nueda, mj.nueda@ua.es

Examples

data("scmp.ob", package = "scMaSigPro")
# Keep the terms with a p-value below 0.01
scmp <- sc.t.fit(scmp.ob, p_value = 0.01, verbose = FALSE)
head(showSol(scmp)[, c("p-value", "R-squared")])
#>              p-value    R-squared
#> Gene2   1.601390e-09 7.260152e-01
#> Gene3   3.985890e-20 8.731628e-01
#> Gene4   1.230465e-66 9.553623e-01
#> Gene9   5.443935e-27 9.012376e-01
#> Gene10 7.184117e-269 9.894360e-01
#> Gene11            NA 3.573648e-12