The multiOmicDataSet object structure

RENEE dataset | The multiOmicDataSet object structure

Default plots from each step | clean | filter | normalize | batch correct | differential expression | filter differential features | Customize plots | 3D PCA | Expression Heatmap | Volcano | Summary | Enhanced | Venn Diagram

It's running so slow! | Understanding the inputs | The input data | The methods we support | Before running ML | The simplest way to run_ml() | Customizing parameters | Changing kfold, cv_times, and training_frac | Custom training indices | Changing the performance metric | Using groups | Controlling how groups are assigned to partitions | More arguments | Case weights | Finding feature importance | Tuning hyperparameters (using the hyperparameter argument) | Other models | Random forest | Decision tree | SVM | Other data | Multiclass data | Continuous data | References

It's running so slow! | Understanding the inputs | The input data | The methods we support | Before running ML | The simplest way to run_ml() | Customizing parameters | Changing kfold, cv_times, and training_frac | Custom training indices | Changing the performance metric | Using groups | Controlling how groups are assigned to partitions | More arguments | Case weights | Finding feature importance | Tuning hyperparameters (using the hyperparameter argument) | Other models | Random forest | Decision tree | SVM | Other data | Multiclass data | Continuous data | References

Handling mothur data | Calculate relative abundances | Taxonomy files | Pooling OTU counts at different taxonomic levels | Distance files | R Markdown helpers for scientific writing | Who doesn't love an Oxford comma? | Human-readable numbers

Summary | Statement of need | mikropml package | Preprocessing data | Running ML | Ideal workflow for running mikropml with many different train/test splits | Tuning & visualization | Dependencies | Acknowledgments | Funding | Author contributions | Conflicts of interest | References

Summary | Statement of need | mikropml package | Preprocessing data | Running ML | Ideal workflow for running mikropml with many different train/test splits | Tuning & visualization | Dependencies | Acknowledgments | Funding | Author contributions | Conflicts of interest | References