PRANA (version 1.0.6)

sigDCGnames: sigDCGnames

Description

A function to retrieve the name of genes that are significantly differentially connected (DC). between two biological/clinical states (aka the main binary indicator) with the presence of additional covariate information.

Usage

sigDCGnames(adjptab, groupvar, alpha)

Value

Names of significantly DC genes (e.g. gene IDs) from PRANA. If you need both adjusted p-values and names, please use sigDCGtab() instead.

Arguments

adjptab

A table with adjusted p-values for all variables that were included in the pseudo-value regression model.

groupvar

Specify the name of binary indicator variable.

alpha

A level of significance (e.g. 0.05).

Examples

#' data(combinedCOPDdat_RGO) # A complete data containing expression and clinical data.

# A gene expression data part of the downloaded data.
rnaseqdat = combinedCOPDdat_RGO[ , 8:ncol(combinedCOPDdat_RGO)]
rnaseqdat = as.data.frame(apply(rnaseqdat, 2, as.numeric))

# A clinical data with additional covariates sorted by current smoking groups:
# The first column is ID, so do not include.
phenodat = combinedCOPDdat_RGO[order(combinedCOPDdat_RGO$currentsmoking), 2:7]

# Indices of non-current smoker (namely Group A)
index_grpA = which(combinedCOPDdat_RGO$currentsmoking == 0)
# Indices of current smoker (namely Group B)
index_grpB = which(combinedCOPDdat_RGO$currentsmoking == 1)

# Use PRANA() function to perform the pseudo-value regression analysis.
# Then, create an object called PRANA_Results to call results.
PRANAres <- PRANA(RNASeqdat = rnaseqdat, clindat = phenodat,
groupA = index_grpA, groupB = index_grpB)

# Next, we want to call the table with adjusted p-values only.
adjptab <- adjpval(PRANAres)

# Please specify the name of binary group indicator in sigDCGnames(groupvar = ).
sigDCGnames <- sigDCGnames(adjptab = adjptab, groupvar = "currentsmoking", alpha = 0.05)
sigDCGnames