Calculates pathway activity with PSF algorithm for provided kegg collection based on expression fold change data.

Usage

psf.from.env.entrez.fc(
  entrez.fc,
  kegg.collection,
  split = TRUE,
  calculate.significance = T,
  bst.steps = 200,
  sum = FALSE,
  map_exp_data = TRUE,
  return_only_signals = FALSE,
  tmm_mode = FALSE,
  tmm_updated_mode = FALSE
)

Arguments

entrez.fc: expression fold change matrix with gene etrez id rownames.
kegg.collection: the list of kegg pathways generated by generate.kegg.collection.from.kgml or generate.kegg.collection function.
split: logical, if true then the incoming signal will be proportionally splitted among the edges.
calculate.significance: logical, if true then function will also calculate significance for the PSF values by shuffling all the network nodes and checking if the resulted PSF values were calculated by chance.
bst.steps: integer, the number of the interations for shuffling and recalculating PSF values for the significance analysis.
sum: logical, default value is FALSE. When set to true pathway activity will be caculated via addition, when set to false then activity willbe calculated via multiplication.
map_exp_data: logical, default value is TRUE. When set to false the expression data will not be mapped into pathway nodes and pathway node expression values will be used instead.
return_only_signals: logical, default value is FALSE. When set to true only PSF values of the pathway nodes will be retruned in the results. Set to TRUE when analyzing large datasets to reduce size of the output file.
tmm_mode: when set to true specific PSF configuration will be used for the pathway activity calculation described in https://www.frontiersin.org/articles/10.3389/fgene.2021.662464/full
tmm_updated_mode: when set to true specific PSF configuration will be used for the pathway activity calculation described in https://www.frontiersin.org/articles/10.3389/fgene.2021.662464/full