GSNA (version 0.1.4.2)

gsnImportCERNO: gsnImportCERNO

Description

Add a CERNO^1 analysis pathways result set to a GSNData object. The data set can be either in the form of a data.frame or specified as import from a delimited text file.

Usage

gsnImportCERNO(
  object,
  pathways_data = NULL,
  filename = NULL,
  id_col = NULL,
  stat_col = NULL,
  sig_order = NULL,
  n_col = NULL,
  sep = "\t"
)

Value

This returns a GSNData object containing imported pathways data.

Arguments

object

A GSNData object.

pathways_data

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

filename

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

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 is normally the ID field of CERNO data, which must be the same as the names of gene sets specified in the tmod object or in the list of gene set vectors specified with the geneSetCollection argument used when building the gene set network. By default this value is 'ID', however if the user has added additional IDs to a CERNO results set, such as GO_ACCESSION, that can be specified here. The IDs 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. By default, this is 'adj.P.val' for CERNO.

sig_order

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

n_col

(optional) Specifies the column containing the number of genes in the gene set. Generally, this is the number of genes in the gene set that are attested in an expression data set (Defaults to 'N1').

sep

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

Details

This method imports a CERNO^1 data set created by the tmod^2 package into a GSNData object.

Note: An error is thrown if all gene set IDs in the genePresenceAbsense are not present in the CERNO 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 CERNO data set column names, and if some are missing, it will throw an error. They can still be imported via gsnImportGenericPathways.

References

1. Zyla J, Marczyk M, Domaszewska T, Kaufmann SHE, Polanska J, Weiner J. Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms. Bioinformatics. 2019;35: 5146–5154. doi:10.1093/bioinformatics/btz447

2. Weiner 3rd J, Domaszewska T. tmod: an R package for general and multivariate enrichment analysis. PeerJ Preprints; 2016 Sep. doi:10.7287/peerj.preprints.2420v1

See Also

gsnAddPathwaysData gsnImportGSEA gsnImportGSNORA gsnImportGenericPathways

Examples

# 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:
sig_pathways.GSN <- gsnImportCERNO( sig_pathways.GSN,
                                    pathways_data = sig_pathways.cerno )