Shijie Zheng

Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age

The paper develops a DNAm reference panel called UniLIFE to estimate immune cell fractions in blood tissue across the lifespan, identifying biomarkers for type-1 diabetes in infants and children.

Pumping the brakes on RNA velocity - understanding and interpreting RNA velocity estimates

This paper analyzes the limitations of RNA velocity estimates by examining how different workflow steps affect the vector field and concludes that RNA velocity should not be interpreted as predicting future cellular states.

A meta-analysis of immune cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

This paper uses DNA methylation data to analyze immune cell fractions across 25,629 blood samples, identifying associations with age, sex, health outcomes, and lifestyle factors.

Universal prediction of cell cycle position using transfer learning

The paper introduces tricycle, a method to predict cell cycle position from single-cell RNA-seq data using transfer learning, showing it generalizes across datasets and species.

recount3: summaries and queries for large-scale RNA-seq expression and splicing

recount3 provides uniformly processed RNA-seq data and tools for querying and analyzing large-scale human and mouse expression and splicing data.

Calling differential DNA methylation at cell-type resolution: avoiding misconceptions and promoting best practices

The paper critiques a recent commentary on DNA methylation analysis, highlighting misconceptions in algorithm evaluation and reporting contradictory results on real EWAS datasets.

Calling differential DNA methylation at cell-type resolution: an objective status-quo

The paper evaluates existing statistical algorithms for identifying cell-type-specific DNA methylation associations in EWAS and highlights biases in prior comparative studies.

ebayGSEA: An improved Gene Set Enrichment Analysis method for Epigenome-Wide-Association Studies

ebayGSEA improves Gene Set Enrichment Analysis for Epigenome-Wide-Association Studies by ranking genes based on differential methylation, enhancing sensitivity and avoiding probe representation bias.

Cell and tissue type independent age-associated DNA methylation changes are not rare but common

The paper analyzes DNA methylation changes across multiple tissues and cell types to show that a substantial portion of age-related methylation changes is shared between different cell/tissue types, with some sites showing age-related alterations in over 10 different types.

Identification of differentially methylated cell-types in Epigenome-Wide Association Studies

This paper introduces CellDMC, a statistical algorithm that identifies differentially methylated cell-types in EWAS, achieving high sensitivity and specificity in detecting methylation changes in smoking-related lung cancer.

Correcting For Cell-Type Heterogeneity In Epigenome-Wide Association Studies: Premature Analyses And Conclusions

This paper critiques a study claiming ReFACTor improves EWAS by correcting cell-type heterogeneity, finding its claims lack rigorous statistical support.

A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies

This paper compares reference-based deconvolution algorithms for correcting cell-type heterogeneity in EWAS, finding that non-constrained methods like robust partial correlations outperform constrained approaches.