George C. Linderman

@MGHSurgery resident | MD/PhD, Applied Mathematics @YaleMed | Working on methods for denoising, analyzing, and visualizing high dimensional datasets.

[Constantin Ahlmann-Eltze (@const-ae.bsky.social) @const_ae]

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 https://t.co/0caikoVmBa and https://t.co/X61ICd6sao

[Harlan Krumholz @hmkyale]

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… https://t.co/z93fXHDaEr @YaleMed @YaleCardiology @CircOutcomes

[dana_peer @dana_peer]

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 --> https://t.co/081H74jhtE

[Amin Karbasi @aminkarbasi]

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):

[George C. Linderman @GCLinderman]

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!

[Dmitry Kobak @hippopedoid]

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]

[Dmitry Kobak @hippopedoid]

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*
https://t.co/jnj0P0gSyc

I think we have finally understood the *real* difference between t-SNE and UMAP. It involves NCE! [1/n]

[Hugh Auchincloss @Awesomecloss]

love making making surgical videos. here's a few of my favorites

[Nik Böhm @jnboehm]

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 https://t.co/3O4B1YxKoM 🧵/16

[Lior Pachter @lpachter]

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!

https://t.co/R55qKVLfvk
https://t.co/ggUNCtEpkm
https://t.co/pKxd5ATgJl
https://t.co/Q7VpCIUb4A

[Anna Neufeld @AnnaCNeufeld]

@lucylgao, @popp_josh, @alexisjbattle, @daniela_witten and I are excited to share our new preprint!(https://t.co/Qee7mSeEwa) We introduce “count splitting”, a flexible framework that allows for valid p-values for differential expression across estimated latent variables. (1/8)

[Pavlos Msaouel @PavlosMsaouel]

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* 👉 https://t.co/yGNPg3yBf2

[Livia Puljak @liviapuljak]

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: https://t.co/4IT2Dgphl4

[Krzakala Florent @KrzakalaF]

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

[Maggie Westfal @MWestfalMDMPH]

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

[Eric Topol @EricTopol]

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" https://t.co/p0z103BnZH

[gokcen @gokcen]

Our single-nucleus cross-tissue cell atlas is out in @ScienceMagazine as part of @humancellatlas: https://t.co/ELvr9Z0gLw w/ Eugene Drokhlyansky, @ShankaraKAnand, @EvgenijFiskin, @ayshwaryas, @francoisaguet, @asegre, Orit Rozenblatt-Rosen, @KArdlie, Aviv Regev & many others 1/

[Lior Pachter @lpachter]

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: https://t.co/yb7sdzWbPQ 🧵 1/

[Gregor Sturm | [email protected] @grsturm]

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.🧵⬇️

https://t.co/rx7rblUWnX

[Cancer Cell @Cancer_Cell]

A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers https://t.co/Y4lg7ColU0

[Dmitry Kobak @hippopedoid]

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: https://t.co/X1ulWty1nM
Code: https://t.co/qLkVD7msEa

Figure below highlights 🇷🇺 and 🇺🇦. /1