R/convertfunctions.R

In jotsetung/mirtarbase-db: miRTarBase miRNA-target interaction database

### functions that convert data.frames to objects and objects to data.frames

######
##
## Report
##
## converts a data.frame into a list of Report classes (length is nrow(data.frame))
## calls unique on the column names required to build a Report object to avoid having duplicate entries in the result.
data.frame2report <- function( x, colname.pmid="references_pmid", colname.support="support_type", colname.experiments="experiments", split="//", do.unique=TRUE ){
    ## check for require columns
    Need <- c( colname.pmid, colname.support, colname.experiments )
    if( class( x )!="data.frame" ){
        stop( "Can only take data.frame as input!" )
    }
    if( sum( colnames( x ) %in% Need )!=length( Need ) ){
        stop( paste0( "One or more required columns (", paste( Need, collapse="," ), ") not found!" ))
    }
    if( do.unique ){
        x <- unique( x[ , Need, drop=FALSE ] )
    }
    ## so, now I'm happy.
    return( apply( x, MARGIN=1, row2report, name.pmid=colname.pmid, name.support=colname.support, name.experiments=colname.experiments, split=split ) )
}
## create a (single row) data.frame for a Report object
report2data.frame <- function( x, colname.pmid="references_pmid", colname.support="support_type", colname.experiments="experiments", split="//", stringsAsFactors=FALSE, row.names=NULL ){
    DF <- data.frame( a=x@pmid, b=paste( x@experiments, collapse=split ), c=x@support_type, stringsAsFactors=stringsAsFactors, row.names=row.names )
    colnames( DF ) <- c( colname.pmid, colname.experiments, colname.support )
    return( DF )
}


######
##
## MTI
##
## converts a data.frame into a list of MTI classes (length is nrow(data.frame))
## Note: we are NOT loding the report slot here!!!
data.frame2mti <- function( x, colname.id="mirtarbase_id", colname.mirna="mirna", colname.mirna.species="species_mirna", colname.target="target_gene", colname.target.entrezid="target_gene_entrez_gene_id", colname.target.species="species_target_gene", do.unique=TRUE ){
    Need <- c( colname.id, colname.mirna, colname.mirna.species, colname.target, colname.target.entrezid, colname.target.species )
    if( class( x )!="data.frame" ){
        stop( "Can only take data.frame as input!" )
    }
    if( sum( colnames( x ) %in% Need )!=length( Need ) ){
        stop( paste0( "One or more required columns (", paste( Need, collapse="," ), ") not found!" ))
    }
    if( do.unique ){
        x <- unique( x[ , Need, drop=FALSE ] )
    }
    return( apply( x, MARGIN=1, row2mti, name.id=colname.id, name.mirna=colname.mirna, name.mirna.species=colname.mirna.species, name.target=colname.target, name.target.entrezid=colname.target.entrezid, name.target.species=colname.target.species ) )
}
## create a data.frame for a MTI object, the number of rows depending on the number of reports.
## if collapse.reports not NULL, the reports will be collapsed (separated by collapse.reports).
mti2data.frame <- function( x, colname.id="mirtarbase_id", colname.mirna="mirna", colname.mirna.species="species_mirna", colname.target="target_gene", colname.target.entrezid="target_gene_entrez_gene_id", colname.target.species="species_target_gene", collapse.reports=NULL, stringsAsFactors=FALSE, row.names=NULL ){
    ## do first the Report stuff...
    reps <- reports( x )
    reps.df <- do.call( "rbind", lapply( reps, report2data.frame, stringsAsFactors=stringsAsFactors ) )
    ## OK, now I have a data.frame with nrow=length(report).
    ## collapsing the reports if wished...
    if( !is.null( collapse.reports ) ){
        reps.df <- data.frame( sapply( reps.df, paste, collapse=collapse.reports, simplify=FALSE ), stringsAsFactors = stringsAsFactors )
    }
    ## defining the data.frame for the MTI.
    mti.df <- data.frame( a=x@id, b=x@mature_mirna, c=x@species_mirna, d=x@target_gene, e=x@target_gene_entrezid, f=x@species_target_gene, stringsAsFactors=stringsAsFactors, row.names=row.names )
    colnames( mti.df ) <- c( colname.id, colname.mirna, colname.mirna.species, colname.target, colname.target.entrezid, colname.target.species )
    ## replicate rows in mti.df to match nrow reps.df
    mti.df <- mti.df[ rep( 1, each=nrow( reps.df ) ), ]
    mti.df <- cbind( mti.df, report_count=reportCount( x ), reps.df, stringsAsFactors=stringsAsFactors )
    return( mti.df )
}


##############
### internal functions!
row2report <- function( x, name.pmid="references_pmid", name.support="support_type", name.experiments="experiments", split="//" ){
    ## here we do not check again for available data etc.
    Rp <- newReport( pmid=as.numeric( x[ name.pmid ] ),
                 support_type=x[ name.support ],
                 experiments=unique( unlist( strsplit( x[ name.experiments ], split=split ) ) )
                 )
    return( Rp )
}
row2mti <- function( x, name.id="mirtarbase_id", name.mirna="mirna", name.mirna.species="species_mirna", name.target="target_gene", name.target.entrezid="target_gene_entrez_gene_id", name.target.species="species_target_gene" ){
    ## here we do not check again for available data etc.
    mti <- newMTI(
        id=x[ name.id ],
        mature_mirna=x[ name.mirna ],
        species_mirna=x[ name.mirna.species ],
        target_gene=x[ name.target ],
        target_gene_entrezid=as.numeric( x[ name.target.entrezid ] ),
        species_target_gene=x[ name.target.species ]
    )
    return( mti )
}