Jing Lei

The paper introduces TIDEST, a framework that improves statistical inference in reconstructed spatial transcriptomes by correcting for prediction errors and spatial confounding, leading to more accurate biological interpretations.

The paper introduces a statistical framework for proteomic data analysis that combines imputation with debiasing to improve inference and reduce false positives in both single-cell and bulk data.

The paper introduces a model-free prediction test to assess if X contributes to predicting Y, showing improved power in gene expression analyses using CITE-seq and spatial transcriptomics data.

The paper identifies cell-type-specific marker genes from bulk transcriptome data by refining published markers using correlation patterns.

This paper introduces eSVD, a non-linear embedding method for single-cell RNA-seq data, and applies it to identify two distinct developmental trajectories in mouse oligodendrocytes.

SOUP, a semi-soft clustering method, identifies pure and transitional cell types from single-cell gene expression data by first detecting pure cells and then estimating membership matrices.

This paper introduces a unified statistical model for single cell and bulk RNA-seq data that improves dropout correction and cell type deconvolution.