Practical Workflows

NoteReal Research, Real Solutions

Practical workflows demonstrate how BigDataStatMeth solves genuine computational challenges. These are complete analyses from start to finish that you can adapt to your own research.

1 Complete Examples

Unlike tutorials that teach concepts in isolation, these workflows present end-to-end analyses. You’ll see how operations combine, how results are validated, and how to handle problems that arise in real research.

2 Available Workflows

TipImplementing PCA

Dimensionality reduction

Principal component analysis for large-scale genomic data. Learn to identify major axes of variation and reveal population structure.

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WarningImplementing CCA

Multi-omics integration

Canonical correlation analysis for integrating different data types. Identify relationships between gene expression and DNA methylation using block-wise algorithms.

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ImportantCross-Platform Workflows

Interoperability

Work seamlessly across different systems and programming languages. Pass data between R, Python, and C++ using HDF5 as a common format.

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CautionAdapt These Patterns

These workflows are designed to be adapted to your research needs. The patterns you learn—how operations are sequenced, how results are validated, how errors are diagnosed—apply across many different analyses.

TipContinue Learning

Ready to develop your own methods? See Developing Methods for detailed implementation examples in both R and C++, or consult the API Reference for function documentation.