Pablo Tamayo

Variable sparsity non-negative matrix and tensor factorization algorithms and their applications in cancer genomics

This paper introduces variable-sparsity matrix and tensor factorization algorithms for cancer genomics to improve biological interpretability and modeling of cancer subtypes and pathways.

The Multi-scale Copula Index (MCI): a new information-theoretic measure of association and its applications in functional cancer genomics

The paper introduces the Multi-scale Copula Index (MCI), an information-theoretic measure of association, and demonstrates its utility in cancer genomics for capturing contextual associations in complex datasets.

Single Nuclei-Derived Molecular Subtypes of Gastrointestinal Stromal Tumors Correlate with Clinicopathologic Features and Predict Clinical Outcomes

The paper identifies six molecular subtypes of GIST using single nucleus RNA sequencing and correlates them with clinicopathologic features and clinical outcomes.

MICAL2 Is a Super Enhancer Associated Gene that Promotes Pancreatic Cancer Growth and Metastasis

This study identifies MICAL2 as a super enhancer-associated gene that promotes pancreatic cancer growth and metastasis through multiple molecular mechanisms.

Data driven refinement of gene expression signatures for enrichment analysis

The paper refines gene expression signatures using multi-omics data to improve enrichment analysis by addressing lack of coordinate expression in gene sets.

Systematic loss of function screens identify pathway specific functional circular RNAs

The paper identifies pathway-specific functional circular RNAs through loss-of-function screens in cancer cell lines, revealing roles in ferroptosis, WNT, and MAPK signaling.

Profiling ranked list enrichment scoring in sparse data elucidates algorithmic tradeoffs

The paper evaluates ssGSEA's performance on scRNA-seq data, finds it unreliable due to sparsity, and identifies UCell as more robust for pseudobulk analysis.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS

The paper introduces computational tools for NMF analysis of single-cell RNA-seq data, demonstrating their use in quantifying cell state transitions in pancreatic cancer.

Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies

This paper introduces REVEALER 2.0, an improved algorithm for identifying genomic alterations associated with functional profiles, showing increased speed and resolution for analyzing cancer genomic data.

Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding

The paper introduces SWNE, a method for visualizing single-cell gene expression data that improves local and global structure preservation compared to t-SNE.