Dealing with too many cells across multi-sample single-cell data? We developed a sketching approach based on Kernel Herding to downsample data, specifically optimized to maintain frequencies across all cell-types. Code: https://t.co/TqdwPsff59. Give it a try! ✨ @JoleneRanek
Exciting week! 🎉 Next Monday, Sneha will defend her honors thesis 'Deciphering the Microglia Transcriptome: Unraveling the Consequences of Early Life Stress and Aging'. Next Tuesday Haidong will defend his PhD 'Set-based modeling and applications in single-cell bioinformatics'.
Our 🪐SATURN method is now out in @naturemethods!
SATURN paves the way for universal cell embeddings, enabling integration of datasets across different species 🐒🐁🧍🐟🐸 Using protein language models, we encode biological meaning of genes in scRNA-seq datasets.
Congratulations to our two amazing rotons Luvna and Yu-Chen for great rotations and for getting the neuroimmune and microglia projects going in the lab! 🧠
Congratulations @alecplot for winning a best poster award at data science day for work predicting CD8 T-cell fates with @JJMilnerLab :) Truthfully, this a picture from last week, but is the same poster. Great job Alec.
Super excited to introduce DELVE (https://t.co/UNiBlLdkKs), an unsupervised feature selection method for improving inference of developmental or disease trajectories from noisy single-cell data: https://t.co/6BvY7kA3WI 😊(1/8)
Have you ever wondered if you could leverage RNA velocity data to better resolve biological trajectories or disease phenotypes?
Delighted to share that our benchmarking study is out today in @GenomeBiology! Biggest thank you to @natstann@purvislab 😊
https://t.co/W3bldwxh3E
Very proud of @JoleneRanek as her task oriented benchmarking study for integrating RNA velocity and gene expression for trajectory inference and outcome prediction is out today in Genome Biology! https://t.co/arTT9qukGv . 🥳
.@haidyi1 and @JoleneRanek are in Chicago @acm_bcb to present their new papers! 🙂🎉
CytoEMD presented by @haidyi1 : https://t.co/zvhHzPPBY8
Distribution-preserving sketching presented by @JoleneRanek : https://t.co/3FEgM1q5HR
Congratulations to Chi-Jane who led our new paper on graph coarsening approaches for single-cell data. We show that by shrinking the graph in a principled way, you can achieve similar performance in downstream bioinf tasks as if using the full graph. https://t.co/971yhTOwvI
Excited to share our new paper (to appear soon in ACM BCB!) about sketching single-cell data. 😀 Specifically, our sketches preserve cell population frequencies and of course rare populations ;), led by Vishal and @JoleneRanek https://t.co/W8LItucOSq
The influx of single-cell data is staggering—not just in volume but in its multiple modalities. How can we optimally combine these modalities to make biologically meaningful predictions? (1/7)
https://t.co/qEaeF5etVd
Happy to share our new work in collaboration with Junier Oliva and lead by Siyuan Shan on encoding and featurizing cellular landscapes with random Fourier features and kernel mean embeddings! ✨
https://t.co/Jy0hXWEo4H
Happy Holidays to all! Grateful for support from students, collaborators, colleagues and mentors in our first 11 months. :) Wishing you lots of CyTOF in 2022.