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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Yunfei Hu's academic homepage
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes
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Given the abundance of the autism-related literature, we were thus motivated to develop Autism_genepheno, a text mining pipeline to identify sentence-level mentions of autism-associated genes and phenotypes in literature through natural language processing methods.
Recommended citation: Li, S., Guo, Z., Ioffe, J.B. et al. Text mining of gene–phenotype associations reveals new phenotypic profiles of autism-associated genes. Sci Rep 11, 15269 (2021). http://oliiverhu.github.io/files/sr2021.pdf
An ensemble deep learning framework to refine large deletions in linked-reads
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In this work, we propose AquilaDeepFilter to filter large deletion SVs from Aquila and Aquila_stLFR. AquilaDeepFilter relies on a deep learning ensemble approach by integrating several state-of-the-art CNN backbones.
Recommended citation: Y. Hu, S. V. Mangal, L. Zhang, X. Zhou. An ensemble deep learning framework to refine large deletions in linked-reads. The IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2021) http://oliiverhu.github.io/files/bibm2021.pdf
Automated filtering of genome-wide large deletions through an ensemble deep learning framework
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After extending the algorithm to shortreads dataset, we tested the performance of AquilaDeepFilter on all five linked-reads and short-read libraries sequenced from the well-studied NA24385 sample, validated against the Genome in a Bottle benchmark. To demonstrate the filtering ability of AquilaDeepFilter, we utilized the SV calls from three upstream SV detection tools including Aquila, Aquila_stLFR and Delly as the baseline.
Recommended citation: Yunfei Hu, Sanidhya Mangal, Lu Zhang, Xin Zhou, Automated filtering of genome-wide large deletions through an ensemble deep learning framework, Methods, Volume 206, 2022, Pages 77-86. http://oliiverhu.github.io/files/methods2022.pdf
Haplotyping-Assisted Diploid Assembly and Variant Detection with Linked Reads
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Motivated by current limitations in generating high-quality diploid assemblies and detecting variants, a new suite of software tools, Aquila, was developed to fully take advantage of linked-read sequencing technology. The overarching goal of Aquila is to exploit the strengths of linked-read technology including long-range connectivity and inherent phasing of variants for reference-assisted local de novo assembly at the whole-genome scale.
Recommended citation: Hu, Y., Yang, C., Zhang, L., Zhou, X. (2023). Haplotyping-Assisted Diploid Assembly and Variant Detection with Linked Reads. In: Peters, B.A., Drmanac, R. (eds) Haplotyping. Methods in Molecular Biology, vol 2590. http://oliiverhu.github.io/files/mimb2022.pdf
ADEPT: Autoencoder with Differentially Expressed Genes and Imputation for a Robust Spatial Transcriptomics Clustering
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To harness both spatial context and transcriptional profile in ST data, we develop a novel graph-based multi-stage framework for robust clustering, called ADEPT. To control and stabilize data quality, ADEPT relies on selection of differentially expressed genes (DEGs) and imputation of the multiple DEG-based matrices for the initial and final clustering of a graph autoencoder backbone that minimizes the variance of clustering results.
Recommended citation: Y. Hu, Y. Zhao, C. T. Schunk, Y. Ma, T. Derr*, X. M. Zhou*. ADEPT: autoencoder with differentially expressed genes and imputation for a robust spatial transcriptomics clustering. (Recomb-seq 2023). http://oliiverhu.github.io/files/recombseq2023.pdf
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
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Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of benchmark studies complicates the selection of methods and future method development. Here we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity.
Recommended citation: Hu, Yunfei, et al. "Benchmarking clustering, alignment, and integration methods for spatial transcriptomics." bioRxiv (2024). https://www.biorxiv.org/content/10.1101/2024.03.12.584114v1
MaskGraphene: Advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning
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To address this, we introduce a method called MaskGraphene, for the purpose of better aligning and integrating different ST slices using both self-supervised and contrastive learning. MaskGraphene learns the joint embeddings to capture the geometric information efficiently. MaskGraphene further facilitates spatial aware data integration and simultaneous identification of shared and unique cell/domain types across different slices.
Recommended citation: Hu, Yunfei, et al. "MaskGraphene: Advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning." bioRxiv (2024): 2024-02. https://www.biorxiv.org/content/10.1101/2024.02.21.581387v1.abstract
CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data.
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To address these challenges, we introduce CNVeil, a robust quantitative algorithm designed to accurately reveal CNV profiles while overcoming the inherent noise and bias in scDNA-seq data. CNVeil incorporates a unique bias correction method using normal cell profiles identified by a PCA-based Gini coefficient, effectively mitigating sequencing bias.
Recommended citation: Yuan, W., Luo, C., Hu, Y., Zhang, L., Wen, Z., Liu, Y. H., ... & Zhou, X. M. (2024). CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data. bioRxiv, 2024-02. https://www.biorxiv.org/content/10.1101/2024.02.21.581409.abstract
talks
BIBM2021 oral presentation
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RECOMB-seq2023 oral presentation
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Gives a talk on our paper “ADEPT: Autoencoder with Differentially Expressed Genes and Imputation for a Robust Spatial Transcriptomics Clustering”
RECOMB-seq2024 oral presentation
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Gives a talk on our paper “MaskGraphene: Advancing joint embedding, clustering, and batch correction for spatial transcriptomics using graph-based self-supervised learning”
teaching
Mentoring Activities
mentoring, Maizie Zhou's Lab, 2021
Mentoring/collaboration experiences
Teaching Assistant
BME3890/BME8901: Special Topics – Computational Genomics. Prof. Maizie Zhou, Vanderbilt University, Biomedical Engineering Department, 2021
In class live coding problem design. Homework Assignments design and grading. Final Project guide and evaluation.
Teaching Assistant
CS4262/CS5262: Foundations of Machine Learning. Prof. Johnathan Sprinkle, Vanderbilt University, Computer Science Department, 2022
Giving lectures. Assignmentss&solutions design.
Teaching Assistant
BME3302: Biomedical Instrumentation II. Prof. Franz Baudenbacher, Vanderbilt University, Biomedical Engineering Department, 2022
Lab instrument and material preparation. Final project presentation evaluation. Assignment grading.