Marco Varrone

255 posts

Marco Varrone

Marco Varrone

@MarcoVarrone

Post-doc and BRIDGE Fellow in Cancer and Computational Biology at @dbc_unil @CirielloLab. Machine Learning for spatial omics.

Katılım Ekim 2014
651 Takip Edilen289 Takipçiler
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Marco Varrone
Marco Varrone@MarcoVarrone·
Excited that our paper "CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity" is finally out! 🎉 A spatial clustering method for any type of spatial proteomics, transcriptomics, or even epigenomics data! nature.com/articles/s4158…
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Marco Varrone
Marco Varrone@MarcoVarrone·
@kenbwork @illumina @10xGenomics Thank you very much! Maybe I am missing something but if it was sterics/geometry, how would thousands of copies be able to fit inside a well?
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Kenny Workman
Kenny Workman@kenbwork·
@MarcoVarrone @illumina @10xGenomics Awesome piece. I'm not sure this is technically correct: "The replication happens so fast that there is not time for another molecule to enter the well, so every well contains copies of only one molecule." My understanding this is more chemistry and sterics/geometry than speed
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Marco Varrone
Marco Varrone@MarcoVarrone·
Lastly, big thanks to @LukasHats for spotting a memory leak in the differential neighborhood enrichment function! This pushed me to refactor it completely. Now it's much faster and more memory-efficient. 🚀 5/5
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Marco Varrone
Marco Varrone@MarcoVarrone·
Key takeaway for choosing a dependency to add to your library. Choose either: 1️⃣ Well-maintained packages (e.g., PyTorch, Scanpy, Seurat). 2️⃣ Simple packages that don't require updates (e.g., tqdm). 3️⃣ Packages you are ready to take over if the original maintenance stops 4/5
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Marco Varrone
Marco Varrone@MarcoVarrone·
🎉 We released CellCharter v0.3.2! - Now part of the @scverse_team ecosystem - More efficient differential neighborhood enrichment - Peak finding for cluster stability - Compatible with the latest PyTorch A few lessons learned along the way 🧵 1/5 github.com/CSOgroup/cellc…
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Marco Varrone
Marco Varrone@MarcoVarrone·
New Batch Effect blog post! 🧬 Finding domains from spatial omics data has exploded in popularity, with over 87 methods! However, two mistakes in their evaluation might be leading us in the wrong direction. batcheffect.com/p/the-pitfalls…
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Marco Varrone
Marco Varrone@MarcoVarrone·
@ItaiYanai That's why every scientist should have a blog for explaining the thought process without going through another round of reviews
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Itai Yanai
Itai Yanai@ItaiYanai·
Is the scientific paper a fraud? Peter Medawar (Nobel Prize winner) suggested that publications are overly structured and formal, giving only a sanitized version of the discovery without revealing the thought process that actually led to it.
Itai Yanai tweet media
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Marco Varrone
Marco Varrone@MarcoVarrone·
The main lesson is whenever you start a new project, start simple, then build up from there. 📈 Sometimes, that’s all it takes.
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Marco Varrone
Marco Varrone@MarcoVarrone·
Neighborhood aggregation proved remarkably effective in capturing crucial spatial relationships while being scalable to millions of cells. It is also the base of CellCharter, the framework we developed during my PhD to study domains from spatial omics data.
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Marco Varrone
Marco Varrone@MarcoVarrone·
New Batch Effect blog post! We often think complex algorithms are the key to analyzing spatial omics data. But sometimes, simplicity can be as effective. 🧵 batcheffect.com/p/when-simple-…
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Marco Varrone
Marco Varrone@MarcoVarrone·
New blog post! We are used to thinking that, when an organism has cancer, all other cells will fight it. We are now realizing that this is not true, and some of these cells may even help it. In this latest Batch Effect post I talk about 4 aspects 🧵 batcheffect.com/p/the-hidden-a…
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Marco Varrone
Marco Varrone@MarcoVarrone·
After a few immunology-focused posts, in the next Batch Effect posts we will shift to more computational gears and see how to actually find cell communities from spatial omics data. Stay tuned!
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Marco Varrone
Marco Varrone@MarcoVarrone·
3. How spatial omics can identify cell communities and true cell interactions to understand what is really happening. 4. How discoveries driven by spatial omics will lead to new effective therapies.
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Marco Varrone retweetledi
Nick Banovich
Nick Banovich@NeBanovich·
Earlier this week we carried out a preliminary analysis of our first @10xGenomics Xenium prime (5,000 gene) run. We had designed this experiment to get a sense of how the prime chemistry performed compared to the Xenium V1 chemistry.
Nick Banovich@NeBanovich

First @10xGenomics Xenium 5K run!

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