Ziqi Zhang

Assistant Professor @UWaterloo CS, single cell data, machine learning

[Ziqi Zhang @Ziqi_Peter]

Our project "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data" is out at Nature Communications. scDisInFact disentangles the perturbation effect from batch effect (1/n)
https://t.co/filRWcschC

[GaTech CSE @GTCSE]

The Class of 2025 is ready to turn study into application! Among the graduates is Ziqi Zhang, who has already made an impact in machine learning and cell gene sequencing!

Meet Ziqi in the profile 👇 and join us in congratulating the #Classof2025! 🎓🥳

https://t.co/dw0t4rwnrQ

[Ziqi Zhang @Ziqi_Peter]

@Francis47543639 @GTCSE Congrats 🎉

[Xiuwei Zhang @XiuweiZhang]

Truly an honor to deliver a keynote talk at the awesome AWSOM workshop! My slides are at https://t.co/ZSpxchLb71 which includes our work on scMoMaT, scDisInFact, scHybridNMF and scMultiSim.

[Ziqi Zhang @Ziqi_Peter]

@_arodriguezca Thank you!

[Ziqi Zhang @Ziqi_Peter]

Our project "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data" is out at Nature Communications. scDisInFact disentangles the perturbation effect from batch effect (1/n)
https://t.co/filRWcschC

[Xiuwei Zhang @XiuweiZhang]

scDisInFact is now published @NatureComms (https://t.co/JUoqcYlItj)! Designed for analyzing scRNA-seq data from multiple conditions (or perturbations) & multiple batches, it performs disentanglement & integration & perturbation prediction. Work by @Ziqi_Peter et al. #singlecell

[Ziqi Zhang @Ziqi_Peter]

@Neda_TV Thank you Neda!

[Ziqi Zhang @Ziqi_Peter]

@elezzx2020 Thank you!

[Ziqi Zhang @Ziqi_Peter]

We also compare the model performance with other perturbation prediction methods in the task of perturbation prediction. Our model shows superior performance, especially in cases where there are strong batch effect (5/n).

[Ziqi Zhang @Ziqi_Peter]

We tested the model on a Glioblastomal dataset and a combined COVID-19 dataset from three different data papers. The model successfully disentangled different sources of variations in these datasets, especially the batch effect and condition effect (4/n).

[Xiuwei Zhang @XiuweiZhang]

LinRace is now published @NatureComms! https://t.co/hGl0s3XpGg We are grateful to the reviewers for their valuable time& constructive comments which helped us to improve the paper substantially, and the editorial team for processing our manuscript very efficiently. @xinhai_pan

[Duen Horng "Polo" Chau @PoloChau]

📢 Georgia Tech's School of Computational Science and Engineering has multiple faculty openings! https://t.co/w6v6KwN07z

Join us at the coolest CSE department in the world! @GTCSE @gtcomputing @mlatgt

[Ziqi Zhang @Ziqi_Peter]

Check out our recent work, scDisInFact(https://t.co/Imoz8hADZx), highlighted in GT news!

[GaTech CSE @GTCSE]

From simulations using Covid-19 datasets to understanding cancerous microenvironments, Asst. Prof. @XiuweiZhang's group is using computational methods to make breakthroughs in single-cell omics research! Check out how!
https://t.co/ZdhY0dTZTw

@Ziqi_Peter @GTResearchNews @mlatgt

[Ziqi Zhang @Ziqi_Peter]

@elezzx2020 @nmancuso_ @StevenGazi Congratulations!

[Xiuwei Zhang @XiuweiZhang]

To analyze scRNA-seq data from a number of patients for disease study, we developed scDisInFact, that disentangles batch effect from variations between data matrices caused by different patient conditions, like disease severity and drug treatments. Work by @Ziqi_Peter et al (1/n)

[Xiuwei Zhang @XiuweiZhang]

Ziqi @Ziqi_Peter won the Best Talk Prize (computational track) for his work "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data" (preprint coming soon)

[Xiuwei Zhang @XiuweiZhang]

Looking forward to speaking at @sydneybioinfo !

[Nature Communications @NatureComms]

@Ziqi_Peter @XiuweiZhang and colleagues present scMoMaT, a mosaic integration method for single cell multi-modality data from multiple batches

https://t.co/pmYCKdXHZV