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List the significant genes

Source: R/sc.filter.R
sc.filter.Rd

sc.filter() lists the genes whose selected model (sc.t.fit()) has an R-squared of at least rsq and a p-value of at most p_value: in one list, one list per term or one list per path (vars), as get.siggenes() of maSigPro does.

Usage

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

A ScMaSigPro object.

rsq

The smallest R-squared of the selected model of a gene.

p_value

The largest p-value of the selected model of a gene; by default that of sc.p.vector().

vars

"all": one list with every gene that passes; "each": one list per term, with the genes whose term passes term_p_value; "groups": one list per path, with the genes with a term of that path that passes it.

intercept

With vars = "groups", the intercept terms that count: "dummy", the dummies of the paths (the differences of their intercepts from the reference); "all", these and the intercept of the reference path; "none", neither, so that only the terms in pseudotime count.

term_p_value

The largest p-value of a term, for vars = "each" and vars = "groups".

includeInflu

Whether to keep the genes with influential observations (see showInflu()).

Value

The object with the gene lists in the Significant slot (class Significant); earlier clusters are removed.

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

See also

plotIntersect() for the overlaps of the lists; Significant.

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

Author

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

Examples

data("scmp.ob", package = "scMaSigPro")
scmp <- sc.filter(scmp.ob, rsq = 0.6, vars = "groups")
res <- scmpResults(scmp)
# The number of genes in each list
c(Path1 = sum(res$sig_Path1), Path2vsPath1 = sum(res$sig_Path2vsPath1))
#>        Path1 Path2vsPath1 
#>           25           40