.. _examples-label: Examples ======== A series of four example notebooks demonstrating the use of ``modality`` are available as a JupyterBook: - `Exploratory analysis `_: shows how you can use modality to perform series of informative exploratory analyses of your data. - `Working with genomic ranges `_: illustrates how you can use ``modality`` to work with genomic range data (for instance a list of CpG islands or exons), and how useful information can be extracted and plotted from these ranges, such as the mean methylation across the range. - `Plotting methylation traces `_: shows how to use ``modality`` to plot methylation traces for a given genomic region. - `Calling differentially methylated regions `_: shows how to call regions that are differentially methylated across a cohort (DMRs). A python script to annotate DMRs and to produce a volcano plot. - See the :doc:`annotation script ` for usage and availability. modality deep dives =================== **Predicting gene expression** - `Part 1: Feature Generation `_: shows how to use modality to extract features for machine learning tasks. - `Part 2: Training and Evaluating the Model `_: shows how to use a set of extracted features and train XGBoost models to predict gene expression. **Predicting chromatin accessibility** - `Part 1: Feature Generation `_: shows how to use modality to extract features for machine learning tasks. - `Part 2: Training and Evaluating the Model `_: shows how to use a set of extracted features and train XGBoost models to predict chromatin accessibility. .. raw:: html