Lulu Chen

swCAM estimates subtype-specific expressions in individual samples using unsupervised deconvolution, revealing changes in cell proportions and gene expression in bipolar disorder.

This study shows that hypothalamic perineuronal nets (PNNs) are regulated by sex, gonadal hormones, and dietary interventions in a region-specific manner.

This paper introduces an unsupervised deconvolution method, CAM3.0, to determine molecular archetype composition and expression from bulk tissues, showing its effectiveness through simulations and case studies.

A metabolomics-transcriptomics study comparing artificial and wild Artemisia rupestris L. to identify molecular differences and their genetic basis.

This paper introduces OVE-FC and OVE-sFC tests to detect subtype-specific and differentially expressed genes, showing improved accuracy in deconvolution compared to existing methods.

COT detects marker genes across subtypes using cosine similarity in a one-sample test, applicable to multi-omics data.

The paper uses cell-type deconvolution to analyze gene expression in schizophrenia and finds upper-layer neurons in the DLPFC are associated with SCZ risk.

This paper proposes two computational methods to integrate multi-omics data and identify disease-specific pathways, demonstrating their effectiveness on muscular dystrophy data.

The paper investigates the glia-neuron ratio in the brain using unsupervised deconvolution methods on single-cell data from human brains.

The paper analyzes the proteomic architecture of human coronary and aortic atherosclerosis using mass spectrometry data from 200 specimens, identifying key protein networks and regulatory features associated with early disease.

The paper develops an unsupervised method to identify marker genes and subpopulations in tumors without prior knowledge of marker genes or subpopulation composition.