sigDCGtab {PRANA}

sigDCGtab

Description

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

Usage

sigDCGtab(adjptab, groupvar, alpha)

Arguments

adjptab

A table with adjusted p-values and names for the variable that the user specifies in the groupvar.

groupvar

Specify the name of binary indicator variable.

alpha

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

Value

Adjusted p-values and names of significantly DC genes (e.g. gene IDs) from PRANA.

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.
adjptab <- adjpval(PRANAres)

# Please specify the name of variable in sigDCGtab(groupvar = ).
sigDCGtab <- sigDCGtab(adjptab = adjptab, groupvar = "currentsmoking", alpha = 0.05)
sigDCGtab