Lingling An

SEMFA infers the statistical significance of Mahalanobis similarity between multi-omics samples using multiple factor analysis, showing effectiveness in both Gaussian and non-Gaussian scenarios.

MiCBuS identifies marker genes for unknown cell types by combining bulk and single-cell RNA-seq data, overcoming limitations of traditional methods.

The paper introduces FAST, a reference-free deconvolution method for spatial transcriptomics data that integrates gene expression, spatial coordinates, and histology information, outperforming existing methods in simulations and real data.

SPADE is a computational method that estimates cell-type proportions in spatial transcriptomics data, showing improved performance over existing methods in synthetic and real-world datasets.

The paper introduces a method using Aitchison distance for microbial source tracking, showing improved accuracy over existing methods in noisy conditions.

scDoc improves scRNA-seq data by correcting drop-out events through cell-to-cell similarity, outperforming existing methods in visualization, subpopulation identification, and differential expression detection.