Are There Any Tools Specific to Handling Sparsity in Cancer Research?
Yes, several computational tools are specifically designed to handle sparsity in cancer research. Examples include:
Seurat: A toolkit for single-cell RNA sequencing data that addresses sparsity through clustering and dimensionality reduction. DESeq2: A tool for differential gene expression analysis that incorporates methods to handle sparse count data. Scikit-learn: A machine learning library that offers various algorithms and preprocessing techniques to manage sparse data.
