Zijian Ni

SKiM discovers and annotates biomedical knowledge using co-occurrence and transformer models to find A-B-C relationships between user-defined concepts.

The paper identifies rare variant polygenic risk scores as more effective than common variant polygenic risk scores in identifying individuals with outlier phenotypes in common human diseases and complex traits.

SpotClean addresses spot swapping in spatial transcriptomics data to improve downstream analysis sensitivity and precision.

SpatialCorr identifies gene sets with spatially varying correlation structure in spatial transcriptomics data, revealing insights not found with existing methods.

The paper tests two models of beta cell dedifferentiation in diabetes using lineage tracing and RNA-seq in mice, showing dedifferentiation is not a reversal of development but varies by diabetes type.

The paper introduces Dino, a normalization method for single-cell RNA-seq data that improves downstream analysis by normalizing the entire gene expression distribution.

CHARTS provides a web tool to analyze and compare tumor subpopulations across public scRNA-seq cancer datasets.

SKiM is a literature-based discovery system that rediscovered biomedical knowledge and identified potential drug candidates by analyzing PubMed literature.

CB2 improves cell identification in 10x Genomics data by leveraging cell-to-cell correlations to distinguish real cells from background barcodes.

Trendy uses segmented regression to analyze gene expression dynamics in ordered experiments, identifying gene-specific patterns and overall transcriptome changes.