# Data Manipulation and Transformation

- [Data Summary](/data-manipulation-and-transformation/data-summary.md): This page provides an overview of the mStat\_summarize\_data\_obj() function, which summarizes key components of a MicrobiomeStat data object.
- [Data Aggregation](/data-manipulation-and-transformation/data-aggregation.md): Data aggregation summarizes microbiome data at broader taxonomic levels. This reveals intuitive compositional patterns and trends.
- [Data Normalization](/data-manipulation-and-transformation/data-normalization.md): Data normalization is an essential step in microbiome data analysis. It helps account for sequencing depth differences and make samples comparable.
- [Data Filtering](/data-manipulation-and-transformation/data-filtering.md): Data filtering selectively removes certain features or samples from the dataset.MicrobiomeStat provides key functions to filter data objects based on criteria.
- [Data Validation](/data-manipulation-and-transformation/data-validation.md): High-quality analysis requires valid, well-formatted data. MicrobiomeStat provides data validation to check format compliance and consistency.
- [Data Combination](/data-manipulation-and-transformation/data-combination.md): In this section, we will cover the key function in MicrobiomeStat for combining multiple microbiome datasets into one object for integrated analysis.
- [Metadata Management](/data-manipulation-and-transformation/metadata-management.md): Metadata provides crucial sample information for microbiome analysis. Effective metadata management enables robust comparative analysis.
- [Color Palette](/data-manipulation-and-transformation/color-palette.md)