Bo Li

PerturbView enables highly multiplexed optical pooled screens in primary cells and tissues by improving barcode amplification, revealing regulators of NFκB signaling and enabling spatial transcriptomics integration.

SCHAF generates spatially resolved single-cell transcriptomes from H&E histology images using vision transformers and adversarial learning, showing good agreement with ground-truth data.

This paper introduces a data-driven quality control framework for single-cell transcriptomics that improves cell retention and biological insight compared to conventional methods.

This study uses single-cell RNA-sequencing to analyze fetal bone marrow development and identifies disruptions in hematopoiesis in Down syndrome.

The paper develops KIRCLE to genotype KIR genes from whole exome sequencing data and analyzes its associations with HLA alleles and clinical traits in TCGA and UK Biobank.

IntroSpect improves HLA binding peptide identification sensitivity by 80% using motif-guided database building, while maintaining high accuracy and FDR control.

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.

Cumulus is a cloud-based framework for analyzing large-scale sc/snRNA-seq data, improving efficiency and scalability while maintaining result quality.

The paper develops a toolbox for scRNA-Seq and snRNA-Seq of fresh and frozen human tumors, testing it across eight tumor types and optimizing protocols for different tissue characteristics.

This study identifies 14 genome-wide significant loci and a polygenic signature for myeloproliferative neoplasms, implicating hematopoietic stem cell function and self-renewal in disease risk.

The paper introduces a method to multiplex single-nucleus RNA-seq using barcoded antibodies, reducing costs and enabling large-scale studies.

This study compares the sensitivity and accuracy of the BGISEQ-500 and Illumina HiSeq platforms for single-cell RNA sequencing using a standardized resource of 468 unique single-cells and 1,297 matched single cDNA samples.

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.