Representation of evaluation results by calculating performance measures for an an NormalyzerResults instance — NormalyzerEvaluationResults • NormalyzerDE

Contains the resulting information from the processing which subsequently can be used to generate the quality assessment report.

Usage

NormalyzerEvaluationResults(nr, categoricalAnova = TRUE)

NormalyzerEvaluationResults(nr, categoricalAnova = TRUE)

Arguments

nr: NormalyzerResults object
categoricalAnova: Should ANOVA be categorical or not

Value

nds Generated NormalyzerEvaluationResults instance

Slots

avgcvmem: Average coefficient of variance per method
avgcvmempdiff: Percentage difference of mean coefficient of variance compared to log2-transformed data
featureCVPerMethod: CV calculated per feature and normalization method.
avgmadmem: Average median absolute deviation
avgmadmempdiff: Percentage difference of median absolute deviation compared to log2-transformed data
avgvarmem: Average variance per method
avgvarmempdiff: Percentage difference of mean variance compared to log2-transformed data
lowVarFeaturesCVs: List of 5 for log2-transformed data
lowVarFeaturesCVsPercDiff: Coefficient of variance for least variable entries
anovaP: ANOVA calculated p-values
repCorPear: Within group Pearson correlations
repCorSpear: Within group Spearman correlations

Examples

data(example_summarized_experiment)
normObj <- getVerifiedNormalyzerObject("job_name", example_summarized_experiment)
#> Input data checked. All fields are valid.
#> Sample check: More than one sample group found
#> Sample replication check: All samples have replicates
#> RT annotation column found (5)
normResults <- normMethods(normObj)
normEval <- NormalyzerEvaluationResults(normResults)