Feifei Xiao

The paper identifies lung cancer susceptibility copy number variants (CNVs) and shows their potential role in genomic instability through association analyses and functional validation.

The paper profiles chromatin accessibility in post-sepsis MDSCs, showing divergent epigenetic trajectories between rapid recovery and chronic critical illness, with MHC class II repression in CCI.

MintCNA improves CNA detection in single-cell multi-omics data by integrating deep learning and multivariate change-point detection, outperforming existing methods in simulations and showing strong correlation with bulk sequencing.

This paper develops a robust method for inferring individualized treatment effects in Mendelian randomization, showing improved coverage and shorter intervals compared to existing methods.

CN-RNN is a deep learning framework for detecting copy number variations from exome sequencing data, showing improved performance over existing methods.

This study identifies prolonged reduced oxidative metabolism and mitochondrial mass in CD66b+ leukocytes from sepsis patients, linked to adverse outcomes and gene silencing.

The paper shows replication stress increases de novo CNVs in Plasmodium falciparum, affecting genes involved in human infection.

This study examines age and sex effects on MDSCs in sepsis using single-cell RNA sequencing, finding differences in expansion, transcriptome, and metabolism.

This study maps epigenetic changes in MDSCs after sepsis, revealing age- and sex-specific chromatin remodeling linked to immune dysfunction.

HapCNV improves CNV detection in low-input haploid sequencing data by using a pseudo-reference to reduce bias and enhance accuracy, especially for short CNVs.

This paper introduces SPADE, a method for identifying spatially variable genes in spatial transcriptomic data, showing improved performance over existing methods in both within and between-group analyses.

OSCAA improves CNV-disease association analysis by integrating CNV detection and association testing in a single step, enhancing accuracy and power.

This paper introduces FLCNA, a statistical method for simultaneous copy number estimation and subclone clustering in scDNA-seq data, showing improved performance over existing methods in breast cancer samples.

This study evaluates methods for estimating correlation coefficients in single-cell sequencing data and finds that filtering zeros and considering drop-out probability improves accuracy.

BMI-CNV improves CNV detection by integrating WES and microarray data across samples, showing better accuracy and identifying lung cancer risk regions.

This paper introduces LDcnv, a novel algorithm that improves copy number variation detection by integrating genomic correlation structure, showing higher accuracy and precision than existing methods.

SCANNER is a web resource that enables biologists to analyze, visualize, and share scRNA-seq data without coding skills, identifying differences in gene expression related to smoking and cancer-associated cells.

The paper introduces a statistical framework to jointly estimate cis-eQTLs and parent-of-origin effects using an orthogonal model for RNA-seq data, validating and discovering novel imprinted genes.