Huidong Chen

Machine Learning Enthusiast 💡👨‍💻🤖#MachineLearning#DeepLearning#AI

[Huidong Chen @hd7chen]

I am beyond excited to share that SIMBA, a versatile single-cell graph embedding method, has been published @naturemethods https://t.co/QyKZA8vRur (1/6)

[Huidong Chen @hd7chen]

@XiuweiZhang We have not extensively explored the relationships between features by calculating dot-products of feature embeddings. But we did observe some intriguing patterns during our experiments. It shows promise but also requires further experiments to validate potential findings.

[Huidong Chen @hd7chen]

@XiuweiZhang Congrats on scMoMaT's publication! Thank you for your interest in SIMBA too!

[Huidong Chen @hd7chen]

@mikelove @naturemethods Thanks for your kind words! I may not fully grasp your question but SIMBA has the capability to consider different gene expression levels and also infer possible missing edges during training. Thus, subtle variations should still be captured and reflected in the final embedding.

[Huidong Chen @hd7chen]

I am beyond excited to share that SIMBA, a versatile single-cell graph embedding method, has been published @naturemethods https://t.co/QyKZA8vRur (1/6)

[Huidong Chen @hd7chen]

@kim_vanuytsel @CReM_Boston @BUMedicine @The_BMC Congrats Kim! 🥳 🎉🎊 This is so exciting!!!

[Jayoung Ryu @JayoungR]

Excited to be writing & publishing a Nature Portfolio "Behind the Paper" blog post for SIMBA! https://t.co/lq0N7FUE0c Hope this helps to convey high-level ideas to more readers.

[Huidong Chen @hd7chen]

@nikos_trasan @ImperialImmuno @NHSEngland @imperialcollege Congrats, Nikos and team! 🙌

[Huidong Chen @hd7chen]

@nikos_trasan @naturemethods Thank you, Nikos!

[Huidong Chen @hd7chen]

@Gavcruzeiro @naturemethods Thank you, Gustavo!

[Huidong Chen @hd7chen]

@gc_yuan @naturemethods @lucapinello Thank you, GC!

[Huidong Chen @hd7chen]

@mikelove @naturemethods Thanks Michael!

[Luca Pinello @lucapinello]

1/6 A little thread to celebrate this milestone! Our SIMBA manuscript has finally seen the light of day, after a journey of more than 2 years. It's been an intense ride, but we're thrilled with the result.

[Nature Methods @naturemethods]

SIMBA learns a co-embedding space of single cells and multiple features such as genes, chromatin accessible regions, and transcription factor binding sequences, boosting performance of various analyses of cellular diversity and regulation. https://t.co/VwWU5QLjTl

[Huidong Chen @hd7chen]

We endured an incredibly rigorous peer-review process, and I am immensely grateful to all my co-authors @JayoungR @vinyard_m @adamlerer @lucapinello who joined me on this journey. The remarkable effort from everyone involved made this achievement a reality. (6/6)

[Huidong Chen @hd7chen]

To ensure practicality and easy adoption, we also built a dedicated website (https://t.co/mk22G54mRw) with detailed documentation and tutorials. Any feedback is welcome! (5/6)

[Guo-Cheng Yuan @gc_yuan]

Our lab is recruiting postdocs on spatial transcriptomics and single-cell multi-omics. Please help spread the word! https://t.co/Y0tof1JeKM

[Clint Miller @clintomics]

Nice overview of emerging spatial genomics, transcriptomics and proteomics technologies and key applications from Jeffrey Moffitt ⁦@Prof_Lundberg⁩ ⁦@hoheyn⁩ ⁦@NatureRevGenet⁩ https://t.co/4IW5cT3Z14