Ning Liu
Bioinformatician · Polo Lab, ACE & SAiGENCI, Adelaide University · spatial & single-cell genomics · deep learning
Adelaide, South Australia
ning.liu@adelaide.edu.au
I’m Ning Liu, a bioinformatician and senior postdoctoral researcher in the Polo Lab at the University of Adelaide (ACE & SAiGENCI). I build computational methods and tools to read how the genome is wired — across spatial transcriptomics, single-cell transcriptomics, epigenomics, and, increasingly, deep learning for biology.
My work spans the full arc from method to mechanism: developing open-source software the community actually uses (such as standR for GeoMx spatial data and hoodscanR for single-cell neighbourhoods), and applying it to understand gene regulation in development, immunity, and disease. Earlier, during my PhD, I established novel links between non-coding genetic variation and target genes through three-dimensional genome organisation (Hi-C).
I completed my PhD in Bioinformatics at the University of Adelaide (Dean’s Commendation for Doctoral Thesis Excellence), and previously worked as a Research Officer at WEHI in Melbourne. I’m an active member of Australia’s bioinformatics community (ABACBS / COMBINE) and enjoy mentoring students and teaching.
Take a look at my publications, my software & projects, or my full CV — and feel free to get in touch.
Research interests
Gene expression controlling cell changes
I study how gene-regulatory programs drive cells to switch identity and state — across development, reprogramming, and disease. By integrating transcriptomic and epigenomic readouts, I aim to pinpoint the regulators and circuits that push a cell from one fate to the next.
AI in epigenomics and transcriptomics
I develop deep-learning and graph-based models that learn from large-scale epigenomic and transcriptomic data — to predict regulatory activity, capture structure in the genome and cells, and turn high-dimensional measurements into interpretable biological insight.
Spatial-omics method development
I build open-source tools and statistical methods for spatial transcriptomics, from quality control and normalisation to neighbourhood and tissue-structure analysis — so that biologists can read gene expression in its native spatial context.