Adriana Ivich

Transcriptomic subtypes in high-grade serous ovarian cancer are driven by tumor cellular composition

This study shows HGSC transcriptomic subtypes are driven by tumor cellular composition rather than intrinsic biology, with high predictive accuracy using deconvolution methods.

The Common Fund Data Ecosystem (CFDE)

The paper describes the Common Fund Data Ecosystem (CFDE), which integrates NIH Common Fund data resources to enable discovery, analysis, and collaboration across heterogeneous datasets.

Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution

This study improves bulk RNA-seq deconvolution by integrating single-cell and single-nucleus datasets through filtering and transformation strategies, achieving near-scRNA-seq accuracy.

Deconvolved tumor adipocyte proportions and high grade serous ovarian carcinoma survival

The paper deconvolves adipocyte proportions from bulk RNA-seq data of HGSOC tumors and finds higher adipocyte content is associated with worse survival.

Characterizing intra- and inter-tumor heterogeneity in ovarian high-grade serous carcinoma subtypes using single-cell and spatial transcriptomics

This study uses single-cell and spatial transcriptomics to analyze intra- and inter-tumor heterogeneity in ovarian HGSC subtypes, identifying spatial patterns and cell-cell communication differences.

A new diagnostic modality for bovine tuberculosis: accurate and robust classification of infected cattle using transcriptomics and machine learning

The paper develops mRNA-based classifiers using RNA-seq and machine learning to accurately distinguish bTB-positive from bTB-negative cattle and other pathogens.

Missing cell types in single-cell references impact deconvolution of bulk data but are detectable

The paper examines how missing cell types in single-cell references affect deconvolution of bulk data and shows that missing cell-type profiles can be detected using non-negative matrix factorization.