
Package index
Metaboprep Object
The main S7 class behind the metaboprep R package along with class helper functions. A container for measurement, sample, and feature data.
-
Metaboprep()
- Metaboprep Object
-
add_layer()
- Add a Layer of Data (internal use)
-
read_metabolon_v1()
- Read Metabolon Data (format 1)
-
read_nightingale_v1()
- Read Nightingale Data (format 1)
-
read_olink_v1()
- Read and Process Olink NPX Data File
-
read_somalogic()
- Read and Process SomaLogic adat file
-
available_data_formats()
- List Available Data Formats
-
export()
- Export Data from a Metaboprep Object
-
export_comets()
- Export Data to `COMETS` format
-
export_metaboanalyst()
- Export Data to `MetaboAnalyst` format
-
export_metaboprep()
- Export Data to `Metaboprep` format
-
summarise()
- Summary Statistics
-
feature_summary()
- Feature Summary Statistics
-
sample_summary()
- Sample Summary Statistics
-
pc_and_outliers()
- Principal Component Analysis
-
tree_and_independent_features()
- Identify Independent Features in a Numeric Matrix
-
quality_control()
- Metabolite Quality Control
-
available_report_templates()
- List Available Report Templates
-
generate_report()
- Generate Output Report
-
batch_normalise()
- Batch Normalisation
-
feature_describe()
- Summary Statistics for Features
-
missingness()
- Estimate Missingness
-
outlier_detection()
- Identify indexes of outliers in data
-
outliers()
- Identify Outliers
-
total_peak_area()
- Estimates total peak abundance
-
continuous_power_plot()
- continuous trait power analysis plot
-
multivariate_anova()
- multivariate analysis
-
cramerV()
- Cramer's V (phi)
-
eval.power.binary.imbalanced()
- Estimate power for a binary variable in an imbalanced design
-
eval.power.cont()
- estimate power for continuous variable
-
find.PA.effect.sizes.2.sim()
- identify effect sizes
-
find.cont.effect.sizes.2.sim()
- identify continuos trait effect sizes
-
imbalanced_power_plot()
- binary trait imbalanced design power analysis plot
-
variable_by_factor()
- ggplot2 violin plot
-
clean_names()
- Standardize Column or Feature Names