@MGHSurgery resident | MD/PhD, Applied Mathematics @YaleMed | Working on methods for denoising, analyzing, and visualizing high dimensional datasets.
New Haven, CT
Joined June 2017
- Tweets902
- Following1K
- Followers1.2K
- Likes1.3K
George C. Linderman retweeted
Incredibly proud to see our benchmark of single-cell preprocessing methods finally published 🥳🥳🎉
We show that despite its theoretical limitations, no other transformation consistently outperforms log(y/s+1).
All details at nature.com/articles/s41592-0… and github.com/const-ae/transfor…
George C. Linderman retweeted
One of most important articles I’ve done… showing the noise in clinic BP measurement is large & makes it impossible to track Rx effects; almost useless in evaluating change from 2 clinic visits. Let me explain… ahajournals.org/doi/abs/10.1… @YaleMed @YaleCardiology @CircOutcomes
George C. Linderman retweeted
1. Thrilled and proud to announce our latest preprint: Spectra -Supervised discovery of interpretable gene programs from single-cell data. It is a factorization method that really works and is already in intensive use across projects in my own lab --> doi.org/10.1101/2022.12.20.5…
George C. Linderman retweeted
As the tradition goes, here is the list of 22 papers I read, learned from, and wished I had been a co-author (in no particular order):
The best way to make an algorithm outperform t-SNE in a benchmark has always been to compare against @scikit_learn's implementation. Glad that it is now being modernized.
Great work and congratulations on a sweet bug fix by @hippopedoid and Weiyi!
In @scikit_learn 1.2 (just out!), manifold.TSNE adopts modern parameter defaults, including O(n) learning rate and PCA init (as argued by @GCLinderman, @Anna_C_Belkina, @pavlinpolicar, @CellTypist, myself, and others).
BTW, that crazy bug 🐞 fix in 1.0 deserves a thread! [1/n]
George C. Linderman retweeted
A very long overdue thread: happy to share preprint led by Sebastian Damrich from @FredHamprecht's lab.
*From t-SNE to UMAP with contrastive learning*
arxiv.org/abs/2206.01816
I think we have finally understood the *real* difference between t-SNE and UMAP. It involves NCE! [1/n]
George C. Linderman retweeted
Ever wondered what image datasets look like if they could be visualized? We have developed a new algorithm for visualization based on contrastive learning. Joint work with @hippopedoid and @CellTypist. The full details are available as a preprint arxiv.org/abs/2210.09879 🧵/16
George C. Linderman retweeted
I've been reading Andrei Okounkov's short and accessible expository articles about the work of this years' four Fields medallists and they are wonderful. Highly recommended reading!
arxiv.org/abs/2207.03867
arxiv.org/abs/2207.03871
arxiv.org/abs/2207.03874
arxiv.org/abs/2207.03875
George C. Linderman retweeted
@lucylgao, @popp_josh, @alexisjbattle, @daniela_witten and I are excited to share our new preprint!(arxiv.org/abs/2207.00554) We introduce “count splitting”, a flexible framework that allows for valid p-values for differential expression across estimated latent variables. (1/8)
George C. Linderman retweeted
1/4 New commentary on the big data paradox, i.e., the phenomenon whereby as the number of patients enrolled in a study *increases*, the probability that the confidence intervals from that study will include the truth *decreases* 👉 bit.ly/3xoEvP4
George C. Linderman retweeted
Our new study shows that data availability statements are not very useful; 1670 (93%) authors who indicated that data are available on request either did not respond or declined to share their data with us. Journal of Clinical Epidemiology: doi.org/10.1016/j.jclinepi.2…
George C. Linderman retweeted
Many theoretical works in ML & high-d stats focus on Gaussian data but why should we care? Real data are definitely not Gaussian, amiright?
Well, it might not be such a bad assumption, see plot 👇! How is this possible? Turns out there are universality properties in high-d 1/2
George C. Linderman retweeted
A final GI rounds to thank IR legend Dr. Mueller. General surgery at MGH is so thankful for all of his help over the last 4 decades! @MGHSurgery @MGHIR1
George C. Linderman retweeted
A big day for life science
The Tabula Sapiens, like a Periodic Table of Human Cells, ~500,000 cells analyzed, 24 tissues / organs @ScienceMagazine
"a broadly useful reference to deeply understand and explore human biology at cellular resolution" science.org/doi/10.1126/scie…
George C. Linderman retweeted
Our single-nucleus cross-tissue cell atlas is out in @ScienceMagazine as part of @humancellatlas: science.org/doi/10.1126/scie… w/ Eugene Drokhlyansky, @ShankaraKAnand, @EvgenijFiskin, @ayshwaryas, @francoisaguet, @asegre, Orit Rozenblatt-Rosen, @KArdlie, Aviv Regev & many others 1/
George C. Linderman retweeted
The analysis of single-cell RNA-seq data begins with "normalizing" counts. In a preprint with @sinabooeshaghi, @IngileifBryndis & @agalvezmerchan, we examine the assumptions and challenges of normalization, benchmark methods, and motivate solutions: biorxiv.org/content/10.1101/… 🧵 1/
George C. Linderman retweeted
I’m happy to share our latest preprint introducing the Single Cell Lung Cancer Atlas (LuCA) integrating >1.2M cells from 223 NSCLC patients + 86 controls from 29 datasets. We used it (among other things) to study tissue-resident neutrophils in NSCLC.🧵⬇️
biorxiv.org/content/10.1101/…
George C. Linderman retweeted
A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers dlvr.it/SQ74b3
George C. Linderman retweeted
My paper on Poisson underdispersion in reported Covid-19 cases & deaths is out in @signmagazine. The claim is that underdispersion is a HUGE RED FLAG and suggests misreporting.
Paper: rss.onlinelibrary.wiley.com/…
Code: github.com/dkobak/covid-unde…
Figure below highlights 🇷🇺 and 🇺🇦. /1
