Juan Xie

The paper provides guidance on analyzing circular data in spatial omics, using case studies to illustrate key considerations and methods.

spaCraft provides calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics by integrating domain recovery and clustering uncertainty.

PRIME-BSPre predicts genome-wide protein-RNA binding sites using templates that integrate RNA sequence, secondary, and tertiary structures, showing strong performance on human RBP datasets.

This paper introduces foundational steps for analyzing spatial transcriptomic data from 10X Visium, focusing on clustering, spatially variable gene discovery, and cell-cell communication.

spaDesign provides a statistical framework to optimize sequencing depth in spatial transcriptomics experiments by simulating data and estimating required sequencing depth for detecting spatial domains.

LRT integrates scTCR-seq and scRNA-seq data to infer T cell trajectories by combining clonal relationships with transcriptomic profiles.

The paper uses spatial transcriptomics and the MAPLE algorithm to analyze fibrotic lung tissue, identifying spatial biomarkers and fibrotic programs in IPF.

CPPred-sORF improves coding potential prediction for sORFs by using non-AUG start codons and achieves high sensitivity and specificity.

The paper introduces RMalign, a size-independent RNA 3D structural alignment tool, which improves benchmarking performance over existing methods and updates the PRIME RNA-protein modeling approach.

Deep-RBPPred predicts RNA binding proteins across multiple species using deep learning, achieving high accuracy and speed compared to existing methods.

QUBIC2 improves biclustering for bulk and single-cell RNA-Seq by addressing sparsity and transcriptional regulation, outperforming existing tools in functional module detection and cell type classification.

GeneQC estimates the reliability of gene expression levels from RNA-Seq data by analyzing mapping uncertainty, improving downstream analysis validity.