GSNA (version 0.1.4.2)

gsnImportGenericPathways: gsnImportGenericPathways

Description

Import a data.frame or text file containing a pathways result set to a GSNData object. The id_col and stat_col should be specified, but if they are not, the function attempts to guess.

Usage

gsnImportGenericPathways(
  object,
  pathways_data = NULL,
  filename = NULL,
  type = "generic",
  id_col = NULL,
  stat_col = NULL,
  stat_col_2 = NULL,
  sig_order = NULL,
  sig_order_2 = NULL,
  n_col = NULL,
  sep = "\t"
)

Value

This returns a GSNData object containing imported pathways data.

Note: An error is thrown if all gene set IDs in the $genePresenceAbsence field are not present in the GSNORA ID column. On the other hand, if there are gene set IDs present in the pathways data that are absent from the genePresenceAbsence matrix, then these methods emit a warning. It also checks for the standard GSNORA data set column names, and if some are missing, it will throw an error.

Arguments

object

A GSNData object.

pathways_data

An (optional) data.frame containing the pathways analysis. (Either this or the filename argument must be set.

filename

An (optional) filename for data sets read from a text file containing pathways results. This is ignored if the pathways_data argument is set.

type

A character vector of length 1 indicating the type of result set. This defaults to 'generic'.

id_col

(optional) A character vector of length 1 indicating the name of the column used as a key for gene sets or modules. This should be the same as the set of names of gene sets in the gene set collection specified by the geneSetCollection argument used in building gene set networks. If not specified, the function will search for "ID", "id", "NAME" & "Term" in the data set's column names, in that order, taking the first one it finds. The values in the column must correspond to the names of the gene sets provided, or an error will be thrown.

stat_col

(optional) A character vector of length 1 indicating the name of the column used as a statistic to evaluate the quality of pathways results. If unspecified, the function uses regular expressions to search for a column that is labeled as a p-value or p-adj.

stat_col_2

(optional) A character vector of length 1 indicating the name of the column used as an optional second statistic to evaluate the quality of pathways results. If unspecified, the value is NULL.

sig_order

(optional) Either 'loToHi' (default) or 'hiToLo' depending on the statistic used to evaluate pathways results.

sig_order_2

(optional) Either 'loToHi' (default) or 'hiToLo' depending on the stat_col_2 statistic used to evaluate pathways results.

n_col

(optional) The name of a pathways data column that contains gene set size information. If unset, the function will scan for the strings 'N1', 'N', 'SIZE', and 'Count', taking the fist one it finds.

sep

A separator for text file import, defaults to "\t". Ignored if the filename argument is not specified.

See Also

gsnAddPathwaysData gsnImportCERNO gsnImportGSEA gsnImportGenericPathways

Examples

library(GSNA)

# In this example, we generate a gene set network from CERNO example
# data. We begin by subsetting the CERNO data for significant results:
sig_pathways.cerno <- subset( Bai_CiHep_DN.cerno, adj.P.Val <= 0.05 )

# Now create a gene set collection containing just the gene sets
# with significant CERNO results, by subsetting Bai_gsc.tmod using
# the gene set IDs as keys:
sig_pathways.tmod <- Bai_gsc.tmod[sig_pathways.cerno$ID]

# And obtain a background gene set from differential expression data:
background_genes <- toupper( rownames( Bai_CiHep_v_Fib2.de ) )

# Build a gene set network:
sig_pathways.GSN <-
   buildGeneSetNetworkJaccard(geneSetCollection = sig_pathways.tmod,
                              ref.background = background_genes )

# Now import the CERNO data. Generic import can work with most types of data,
# and we can manually specify id_col, stat_col, n_col:
sig_pathways.GSN <- gsnImportGenericPathways( sig_pathways.GSN,
                                              pathways_data = sig_pathways.cerno,
                                              id_col = 'ID',
                                              stat_col = 'adj.P.Val',
                                              sig_order = 'loToHi',
                                              n_col = 'N1'
                                               )