Man pages for MRCIEU/metaboprep
Metabolomics data preparation and processing pipeline
| batch_normalization | median batch normalization |
| cramerV | Cramer's V (phi) |
| eval.power.binary | Estimate power for a binary variable |
| eval.power.binary.imbalanced | Estimate power for a binary variable in an imbalanced design |
| eval.power.cont | estimate power for continuous variable |
| feature.describe | summary statistics for features |
| feature.missingness | estimate feature missingness |
| feature.outliers | outlier sample count for a features |
| feature_plots | feature plots to file |
| feature.sum.stats | feature summary statistics |
| feature.tree.independence | identify independent features |
| find.cont.effect.sizes.2.sim | identify continuos trait effect sizes |
| find.PA.effect.sizes.2.sim | identify effect sizes |
| generate_report | generate metaboprep summary html report |
| greedy.pairwise.n.filter | greedy selection |
| id.outliers | identify outliers |
| make.cor.matrix | correlation matrix |
| make.tree | generate a hclust dendrogram |
| median_impute | median impute missing data |
| met2batch | batch effect on numeric matrix |
| missingness.sum | missingness summary plots |
| multivariate.anova | multivariate analysis |
| ng_anno | Nightingale Health metabolomics annotation data set |
| outlier.matrix | identify outlier sample indexes in a matrix |
| outliers | identify outliers |
| outlier.summary | feature summary plots |
| pca.factor.analysis | PCA factor analysis and annotation enrichment |
| pc.and.outliers | principal component analysis |
| pcapairs_bymoose | pca pairs plot |
| perform.metabolite.qc | perform metabolomics quality control |
| read.in.metabolon | read in Metabolon (v1) metabolomics data |
| read.in.nightingale | read in Nightingale Health metabolomics data |
| rntransform | rank normal tranformation |
| run.cont.power.make.plot | continuous trait power analysis plot |
| run.pa.imbalanced.power.make.plot | binary trait imbalanced design power analysis plot |
| sam.missingness.exclusion | sample exlusions on missingness and total peak area |
| sample.missingness | estimate sample missingness |
| sample.outliers | outlier features count for samples |
| sample.sum.stats | summary statistics for samples |
| total.peak.area | estimates total peak abundance |
| tree_and_independent_features | identify independent features in a numeric matrix |
| variable.by.factor | ggplot2 violin plot |
