Jiadong Mao

DIVAS: an R package for identifying shared and individual variations of multiomics data

DIVAS identifies shared and individual variations in multiomics data, revealing partially shared patterns missed by existing methods.

{Phi}-Space ST: a platform-agnostic method to identify cell states inspatial transcriptomics studies

Phi-Space ST identifies continuous cell states in spatial transcriptomics data using scRNA-seq references, revealing niche-specific cell types and patterns in cancer tissues.

Multivariate integration of histological images and gene expression data: a comparative review

This paper reviews multivariate methods for integrating histological images and gene expression data, showing they capture distinct yet complementary aspects of the underlying information.

phylobar: an R package for multiresolution compositional barplots in omics studies

phylobar provides an R package for interactive multiresolution compositional barplots to visualize hierarchical omics data, enabling exploration across taxonomic resolutions.

CellDiffusion: a generative model to annotate single-cell and spatial RNA-seq using bulk references

CellDiffusion enhances single-cell and spatial RNA-seq annotation by generating virtual cells to bridge the gap with bulk RNA-seq references, improving accuracy and revealing new biological insights.

Oral microbial signatures of head and neck cancer patients highlight diverse longitudinal patterns of oral mucositis severity

The paper identifies distinct microbial patterns and clusters associated with varying oral mucositis severity trajectories in head and neck cancer patients.

NeighbourNet: Scalable cell-specific co-expression networks for granular regulatory pattern discovery

NeighbourNet constructs cell-specific co-expression networks to uncover regulatory patterns in single-cell data, improving efficiency and enabling downstream analysis.

StableMate: a new statistical method to select stable predictors in omics data

StableMate identifies stable predictors across omics datasets to improve reproducibility and interpretability of biological associations.

{Phi}-Space: Continuous phenotyping of single-cell multi-omics data

Phi-Space continuously phenotypes single-cell multi-omics data by embedding cells in a low-dimensional space defined by reference phenotypes, enabling robust annotation and analysis of transitional and novel cell states.