Publications

Functional genomics analysis identifies loss of HNF1B function as a cause of Mayer–Rokitansky–Küster–Hauser syndrome

Published in Human Molecular Genetics, 2022

Here, we conducted a microarray analysis of 13 women affected by MRKH syndrome, resulting in the identification of chromosomal changes, including the deletion at 17q12, which contains both HNF1B and LHX1. We ablated Hnf1b specifically in the epithelium of the Müllerian ducts in mice and found that this caused hypoplastic development of the uterus, as well as kidney anomalies, closely mirroring the MRKH type II phenotype. Our results support the investigation of HNF1B in clinical genetic settings of MRKH syndrome and shed new light on the molecular mechanisms underlying this poorly understood condition in women’s reproductive health.

Recommended citation: Thomson, Ella, et al. (2022). "Functional genomics analysis identifies loss of HNF1B function as a cause of Mayer–Rokitansky–Küster–Hauser syndrome; Human Molecular Genetics. https://doi.org/10.1093/hmg/ddac262

A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages

Published in Frontiers in Immunology, 2022

This paper is about framework called STRISH to study cell-cell interaction through ligand-receptor

Recommended citation: Tran et al. (2022). "A robust experimental and computational analysis framework at multiple resolutions, modalities and coverages." Frontiers in Immunology vol 13. 1664-3224. https://doi.org/10.3389/fimmu.2022.911873

Benchmarking of ATAC Sequencing Data From BGIs Low-Cost DNBSEQ-G400 Instrument for Identification of Open and Occupied Chromatin Regions

Published in Front Mol Biosci, 2022

Here, we benchmark data from Beijing Genomics Institutes (BGI) DNBSEQ-G400 low-cost sequencer against data from a standard Illumina instrument (HiSeqX10). For comparisons, the same bulk ATAC-seq libraries generated from pluripotent stem cells (PSCs) and fibroblasts were sequenced on both platforms.

Recommended citation: Naval-Sanchez et al. (2022). "Benchmarking of ATAC Sequencing Data From BGI’s Low-Cost DNBSEQ-G400 Instrument for Identification of Open and Occupied Chromatin Regions" Frontiers in Molecular Biosciences vol 9. (2296). https://doi.org/10.3389/fmolb.2022.900323

Spatial single-cell analysis of colorectal cancer tumour using multiplexed imaging mass cytometry

Published in Journal for ImmunoTherapy of Cancer, 2020

Using Hyperion Imaging Mass Cytometry (IMC), we simultaneously profiled 16 protein markers for each tissue section, capturing molecular signatures of tissue architecture, cancer cells, and immune cells. In this project we aim to capture tissue morphology, cancer cell types, multi-parameter protein contents of single cells in within morphologically intact tissue sections of colorectal tumours from 52 patients.

Recommended citation: Tran M, Su A, Lee H, et (2020). "Spatial single-cell analysis of colorectal cancer tumour using multiplexed imaging mass cytometry" Journal for ImmunoTherapy of Cancer. 665. https://jitc.bmj.com/content/8/Suppl_3/A399.1

SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells

Published in Bioinformatics, 2020

Using Spatial Transcriptomics technology, we develop a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially barcoded spots in a tissue.

Recommended citation: Xiao Tan, Andrew Su (2020). "SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells" Bioinformatics, Vol 36, . (2293–2294). https://doi.org/10.1093/bioinformatics/btz914

Machine learning approaches for comparative genome structure analysis

Published in American Society of Human Genetics, 2018

We introduce a convolutional autoencoder an unsupervised machine learning technique to produce a similarity function to compare areas between pairs of Hi-C matrices

Recommended citation: Rojas and Tran (2018). "Machine learning approaches for comparative genome structure analysis." American Society of Human Genetics. https://www.ashg.org/wp-content/uploads/2019/10/2018-poster-abstracts.pdf

Centroid Neural Network with Pairwise Constraints for Semi-supervised Learning

Published in Neural Processing Letters, 2018

This paper combines pairwise constraint to unsupervised clustering algorithm to produce a semi-supervised algorithm called CNN-PC

Recommended citation: Tran et al. (2018). "Centroid Neural Network with Pairwise Constraints for Semi-supervised Learning." Neural Processing Letters vol 48. 1721–1747. https://link.springer.com/article/10.1007/s11063-018-9794-8

Minh Tran