# entity objects used in struct, to reduce duplicity ents=list() ents$alpha=entity(name='Confidence level', ontology='STATO:0000053', value=0.05, type='numeric', description='The p-value cutoff for determining significance.' ) ents$mtc=enum(name='Multiple test correction method', value='fdr', type='character', description=c( 'bonferroni' = 'Bonferroni correction in which the p-values are multiplied by the number of comparisons.', 'fdr' = 'Benjamini and Hochberg False Discovery Rate correction.', 'none' = 'No correction.' ), allowed=c("bonferroni","fdr", "none"), max_length = 1, ontology='OBI:0200089' ) ents$formula=entity(name='Formula', value=y~x, type='formula', description='A symbolic description of the model to be fitted.' ) ents$f_statistic=entity(name='F-statistic', ontology='STATO:0000176', type='data.frame', description=paste0('The value of the calculated statistic.'), value=data.frame() ) ents$p_value=entity(name='p value', ontology='STATO:0000175', type='data.frame', description=paste0('The probability of observing the calculated statistic ', 'if the null hypothesis is true.'), value=data.frame() ) ents$significant=entity(name='Significant features', #ontology='STATO:0000069', type='data.frame', description=paste0('True/False indicating whether the p-value computed ', 'for each variable is less than the threshold.'), value=data.frame() ) ents$blank_label=entity(name = 'Blank label', description = 'The label used to identify blank samples.', value = 'Blank', type='character') ents$qc_label=entity(name = 'QC label', description = paste0('The label used to identify QC samples. If set to NULL ', 'then the median of the samples is used.'), value = 'QC', type=c('character','NULL')) ents$filtered=entity(name = 'Filtered DatasetExperiment', description = 'A DatasetExperiment object containing the filtered data.', type='DatasetExperiment', value=DatasetExperiment() ) ents$flags=entity(name = 'Flags', description = 'A flag indicating whether the feature was rejected or not.', type='data.frame', value=data.frame() ) ents$factor_name=entity(name='Factor name', description='The name of a sample-meta column to use.', type='character', value='V1') ents$factor_names=entity(name='Factor name(s)', description='The name of sample meta column(s) to use.', type='character', value='V1') ents$by_sample=entity(name='Plot by sample or by feature', value=TRUE, type='logical', description=c( 'TRUE' = 'Missing values are plotted per sample.', 'FALSE' = 'Missing values are plotted per feature.' ) ) ents$label_outliers=entity(name='Label outliers', value=FALSE, type='logical', description=c( 'TRUE' = 'Sample labels for potential outliers are displayed on the plot', 'FALSE' = 'Sample labels are not included on the plot.' ) ) ents$show_counts=entity(name='Show counts', value=TRUE, type='logical', description=c( 'TRUE' = 'The number of samples for each box is displayed.', 'FALSE' = 'The number of samples for each box is not displayed.' ) )