R/blank_filter_class.R

In computational-metabolomics/structToolbox: Data processing & analysis tools for Metabolomics and other omics

#' @eval get_description('blank_filter')
#' @examples
#' D = iris_DatasetExperiment()
#' M = blank_filter(fold_change=2,
#'                  factor_name='Species',
#'                  blank_label='setosa',
#'                  qc_label='versicolor')
#' M = model_apply(M,D)
#' @export blank_filter
blank_filter = function(fold_change=20,blank_label='blank',qc_label='QC',factor_name,fraction_in_blank=0,...) {
    out=struct::new_struct('blank_filter',
        fold_change=fold_change,
        blank_label=blank_label,
        qc_label=qc_label,
        factor_name=factor_name,
        fraction_in_blank=fraction_in_blank,
        ...)
    return(out)
}


.blank_filter<-setClass(
    "blank_filter",
    contains = c('model'),
    slots=c(fold_change='entity',
        blank_label='entity',
        qc_label='entity',
        factor_name='entity',
        fraction_in_blank='entity',
        filtered='entity',
        flags='entity'
    ),
    prototype=list(name = 'Blank filter',
        description = paste0('A blank filter filters features by comparing the ',
            'median intensity of blank samples to the median intensity of ',
            'samples. Features where the relative intensity (fold change) is not ',
            'large when compared to the blank are removed. The number of times a ',
            'feature is detected across all blank samples may also be considered. ',
            'If the feature is not detected in a high enough proportion of the ',
            'blanks then it is not removed.'),
        type = 'filter',
        predicted = 'filtered',
        libraries='pmp',
        .params=c('fold_change','blank_label','qc_label','factor_name',
            'fraction_in_blank'),
        .outputs=c('filtered','flags'),
        
        blank_label=ents$blank_label,
        qc_label=ents$qc_label,
        factor_name=ents$factor_name,
        
        fold_change=entity(name = 'Fold change threhsold',
            description = paste0('Features with fold change less than this ',
                'value are removed.'),
            value = 20,
            type='numeric'),
        fraction_in_blank=entity(name='Fraction in blank',
            description=paste0('Features present in less than this proportion ',
                'of the blanks are not considered for removal.'),
            type='numeric',
            value=0),
        
        filtered=ents$filtered,
        flags=ents$flags
    )
)

#' @export
#' @template model_train
setMethod(f="model_train",
    signature=c("blank_filter","DatasetExperiment"),
    definition=function(M,D)
    {
        smeta=D$sample_meta
        x=D$data
        # remove = NULL does not remove blanks. ANY VALUE removes blanks.
        blank_filtered = pmp::filter_peaks_by_blank(t(x), fold_change=M$fold_change,
            classes=smeta[,M$factor_name],
            blank_label=M$blank_label,
            qc_label=M$qc_label,
            remove_samples=FALSE,
            remove_peaks=FALSE,
            fraction_in_blank=M$fraction_in_blank
        )
        
        # store the flags
        flags=data.frame(attributes(blank_filtered)$flags)
        output_value(M,'flags') = data.frame(flags,stringsAsFactors = F)
        return(M)
    }
)

#' @export
#' @template model_predict
setMethod(f="model_predict",signature=c("blank_filter","DatasetExperiment"),
    definition=function(M,D) {
        # remove the blanks. do it this way because pmp doesnt remove from class labels.
        RB = filter_smeta(mode='exclude',levels=M$blank_label,factor_name=M$factor_name)
        RB=model_apply(RB,D)
        D=predicted(RB)
        
        # get the flags
        flags=M$flags
        
        # filter
        D=D[,flags$blank_flags==1,drop=FALSE]
        
        # store
        M$filtered=D
        
        return(M)
    }
)

##### plots
#' @eval get_description('blank_filter_hist')
#' @import struct
#' @export blank_filter_hist
#' @examples
#' C = blank_filter_hist()
blank_filter_hist = function(...) {
    out=.blank_filter_hist()
    out=struct::new_struct('blank_filter_hist',...)
    return(out)
}


.blank_filter_hist<-setClass(
    "blank_filter_hist",
    contains=c('chart'),
    prototype = list(name='Histogram of blank filter fold changes',
        description=paste0('A histogram of the calculated fold changes for ',
        'the blank filter (median samples divided by median blanks)'),
        type="histogram",
        ontology='STATO:0000169'
    )
)


#' @export
#' @template chart_plot
setMethod(f="chart_plot",
    
    signature=c("blank_filter_hist",'blank_filter'),
    definition=function(obj,dobj)
    {
        t=param_value(dobj,'fold_change')
        A=output_value(dobj,'flags')
        A$fold_change=log2(A$fold_change)
        A$features=factor(A$blank_flags,levels=c(1,0),labels=c('accepted','rejected'))
        
        out=ggplot(data=A, aes_(x=~fold_change,fill=~features)) +
            geom_histogram(boundary=log2(t),color='white') +
            xlab('log2(fold change)') +
            ylab('Count') +
            scale_fill_Publication()+
            theme_Publication(base_size = 12) +
            ggtitle('Blank filter')
        theme(panel.border = element_rect(linetype = "solid", fill = NA))
        
        # get the breaks
        po=ggplot_build(out)
        breaks=po$layout$panel_scales_x[[1]]$get_breaks()
        
        # add second axis with labels
        out=out+scale_x_continuous(breaks=breaks,sec.axis=dup_axis(labels=2^breaks,name='Fold change'))
        
        return(out)
    }
)