Lingfei Wang

Airqtl accelerates single-cell eQTL mapping and enables cell state-specific causal gene regulatory network inference using efficient linear mixed models.

The paper identifies genetic variants associated with plasma cortisol and cardiovascular disease, revealing gene networks in liver and adipose tissues regulated by corticosteroid binding globulin and the glucocorticoid receptor.

Dictys infers dynamic gene regulatory networks from single-cell multi-omics data, revealing context-specific and developmental GRNs in human blood and mouse skin.

Normalisr normalizes and tests associations in single-cell CRISPR screens and co-expression data, improving sensitivity and specificity by removing nonlinear confounding.

The paper introduces a method for inferring high-dimensional Bayesian gene networks using genetic node ordering, showing improved efficiency and accuracy over conventional methods.

The paper uses Findr to infer causal gene networks from transcriptomic data, identifying major hub genes in the Geuvadis dataset.

The paper infers causal gene regulatory networks using enhancer RNA expression as a causal anchor and validates predictions against experimental data.

The paper introduces lassopv, a method to control false discoveries in Bayesian gene networks using p-values from lasso regression, achieving optimal false discovery control compared to existing methods.

The paper introduces Findr, a method for causal inference between gene expression traits using genome-transcriptome data, outperforming existing methods in accuracy and speed.