Joined October 2012
- Tweets 178
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Photos and videos
3 Nov 2025
Excited to share BrainSTEM, a two-tier mapping framework for single-cell data of midbrain cultures. We first map to a human fetal whole brain atlas, then refine to a midbrain subatlas to benchmark mDA identity and brain region fidelity. #scRNA #Parkinsons doi.org/10.1126/sciadv.adu79…
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3 Nov 2025
BrainSTEM provides open tools to improve PD modelling and cell therapy optimisation. It flags off target DA like cells that inflate reported mDA yields. This matters because high purity mDA products are essential for safe and effective PD cell replacement.
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3 Nov 2025
This is a tight collaboration with Alfred Sun, following many rounds of discussion on how single-cell omics can improve the quality of mDA differentiation, a key focus of his lab for PD. Many thanks also to @PengyiYang82 and @carissaynchen for their contributions and advice.
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John Ouyang retweeted
22 Aug 2025
Most PhD students stare at a blank page for months.
They have smart ideas but no mindmap.
The difference between finishing and forever-editing?
A bulletproof thesis structure.
Here's what successful PhDs know from day one:
ALT PhD Thesis Mindmap
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21 May 2025
ShinyCell2: An extended library for simple and sharable visualisation of spatial, peak-based and multi-omic single-cell data biorxiv.org/content/10.1101/…
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6 May 2025
Try ShinyCell2! Extended to support spatial, scATAC and multiomics data now!
Want to visualize & create a website for your omics data ( CITEseq scATACseq #SpatialTranscriptomics)?
Try ShinyCell2!😆
Compatible with data formats Seurat Signac ArchR Scanpy (other formats converted by sceasy) (also imaging-based ST data?😁)
vs cellxgene WebAtlas Vitessce
github.com/the-ouyang-lab/Sh…
@OwenRackham @imouyang bioRxiv 2024
biorxiv.org/content/10.1101/…
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24 Apr 2025
Excited to launch ShinyCell2, which now supports interactive visualisation of spatial data, scATAC-seq and multimodal single-cell data! We also made quality of life improvements to existing plots. biorxiv.org/content/10.1101/…
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John Ouyang retweeted
15 Oct 2024
Very excited to present STAMP: An Interpretable spatially aware dimension reduction of spatial transcriptomics nature.com/articles/s41592-0…
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9 Oct 2024
Excited to share scPanel, a computational tool to identify sparse biomarker panels for patient stratification (think disease severity, treatment response) from #singlecell data. Developed by talented @Yi_Carissa_Xie and co-supervised with @Enrico_Petretto. tinyurl.com/scpanelpaper
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23 Sep 2024
1/4 Excited to launch #BrainSTEM, a framework for assessing transcriptional fidelity in midbrain cultures using #singlecell data. As neural systems grow in complexity, our approach adapts computational practices to match the biological scale via a two-tier projection approach.
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23 Sep 2024
3/4 This two-tier projection mirrors iterative clustering, key for cell type annotation in large datasets. BrainSTEM ensures precise mapping without spurious assignments by distinguishing midbrain-specific neural cells, allowing for refined cellular insights in midbrain cultures.
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23 Sep 2024
4/4 The wholebrain and midbrain atlases are available for browsing at brainstem.ouyanglab.com and preprint at tinyurl.com/scbrainstem. This work was spearheaded by myself and Alfred Sun, who is great company to work with and many thanks to @PengyiYang82 for being in this work.
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17 Sep 2024
Some fibroblasts are more equal than others.
16 Sep 2024
Classifying cancer-associated fibroblasts—The good, the bad, and the target dlvr.it/TDJ8xm
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John Ouyang retweeted
19 Apr 2024
👏🏾This is a transformative reduction in long read (ONT) sequence error rate - changes the game. Congrats Mile!
19 Apr 2024
Ready for >Q30 ONT reads? Herro error correction now supports both R9.4.1 and R10.4.1. simplex reads w/ @domstanojevic @DehuiLin (CHM13 -R9.4.1) #nanopore #longreads
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John Ouyang retweeted
11 Apr 2024
How to pick a problem. Today in @CellCellPress. 1/53
cell.com/cell/fulltext/S0092…
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15 Apr 2024
Excited to share this new tool named scPanel by @charissa_yi_xie with @Enrico_Petretto , which identifies sparse gene sets for patient classification from #singlecell data. ScPanel will help identify biomarkers from population-level single-cell studies. tinyurl.com/bddy9ex4
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15 Apr 2024
scPanel comprises three steps: identifying the cell population most responsive to perturbations, determining the minimal number of genes to separate patient groups, and performing patient-level classification by aggregating cell-level prediction probabilities.
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