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Prikvačeni tweet
Our huge Patch-seq effort is out on bioRxiv: https://www.biorxiv.org/content/10.1101/2020.02.03.929158v1 …. 1320 neurons in mouse motor cortex patched, sequenced, and mapped to a scRNA-seq atlas. 642 reconstructed morphologies! Our running joke was that it was a bit like catching Pokemons. (1/n)pic.twitter.com/22vAQLyfKT
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Dmitry Kobak proslijedio/la je Tweet
proud of all papers from my lab - but this one in particular! patch-seq on >1,300 neurons from mouse motor
#cortex yields ephys & anatomy for >70#transcriptomic#neuron types. with@hippopedoid@FedericoScala7@AToliasLab@LHartmanis and many others! https://www.biorxiv.org/content/10.1101/2020.02.03.929158v1 …pic.twitter.com/fwIFWBDno8
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This was submitted back-to-back with a similar Patch-seq effort from the Allen institute, independently done in the visual cortex. We only saw each other's drafts two weeks ago. They only study interneurons but have more of them: https://www.biorxiv.org/content/10.1101/2020.02.03.932244v1 …. (6/n)
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Perhaps most importantly, we find a lot of continuous morpho-electric variation *within* each family, as one traverses the transcriptomic landscape. Given that transcriptomically the t-types are often not well-separated either, we question that t-types are really discrete. (5/n)pic.twitter.com/d8cAsEc0EW
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We to t-SNE and kNN classification on electrophyisological, morphological, and combined features, and find that big neuronal families (Pvalb, Sst, Vip, Lamp5, IT, PT, CT) have largely distinct morpho-electric phenotypes with little overlap.pic.twitter.com/O57kAgQiqL
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This dataset allowed us to provide a morphological and electrophysiological description of most transcriptomic cell types in mouse motor cortex. This is a figure from the paper itself, but there is also a supplementary PDF showing all our cells for each t-type. (3/n)pic.twitter.com/K6kewdyulO
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Most neurons were patched by
@FedericoScala7 in@AToliasLab in Houston. All sequencing performed by@LHartmanis in@sandberglab in Stockholm. Computational analysis done in the lab of@CellTypist in Tuebingen. Reference transcriptomic atlas provided by the Allen institute. (2/n)Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
I must confess that I could never *really* understand VAEs... so ended up thinking about them as autoencoders that inject noise into the bottleneck as some sort of implicit regularization strategy. Well, turns out this might actually be a reasonable way to think about them!https://twitter.com/tetraduzione/status/1222217690397306880 …
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A fascinating account of the life in Bell Labs in the late 1990s and the Schön scandal (a postdoc fabricated data and published 16 [!!] first-author Nature+Science papers on semiconductors within two years; his PI later claimed he did not know and did not realize).https://twitter.com/gottapatchemall/status/1222764624489041921 …
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Dmitry Kobak proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Dmitry Kobak proslijedio/la je Tweet
My blog post from 2017 which I converted to a bioRxiv pre-print last year has now been peer reviewed and published in Nature Biotechnology
@NatureBiotech: https://www.nature.com/articles/s41587-019-0379-5 … "Droplet scRNA-seq is not zero-inflated"pic.twitter.com/gLe9J0uczP
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Dmitry Kobak proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Dmitry Kobak proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Somebody posts a catchy video of a chimp with a "super-human" short-term memory performance; 100K likes, 35K retweets. Somebody else replies that this paper has been debunked, humans can easily do better; 150 likes, 10 retweets. THREE ORDERS OF MAGNITUDE less attention.https://twitter.com/bradpwyble/status/1213944207389478912 …
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Dmitry Kobak proslijedio/la je Tweet
I created an interactive visualization of 150000+ butterflies in the
@NHM_London using deep learning, t-SNE and data from@NHM_Digitise! Click here for the interactive version: https://marian42.de/butterflies/ pic.twitter.com/48MiOQTAj9Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Dmitry Kobak proslijedio/la je Tweet
This is my experience too. tSNE is pretty good given a suitable initialization. And given proper parameters (https://www.nature.com/articles/s41467-019-13056-x …) it can be really great. UMAP is pretty good too but often uses too much whitespace.https://twitter.com/hippopedoid/status/1207999178015727616 …
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Dmitry Kobak proslijedio/la je Tweet
Not surprised that UMAP's preservation of global structure is entirely due to initialization. Force layouts (used by UMAP & t-SNE) do local updates only. A contrario, Laplacian eigenmaps and PCA use the global structure. Init globally and fine-tune locally is the wining strategy.https://twitter.com/hippopedoid/status/1207999178015727616 …
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But to decide which algorithm is more faithful to the single-cell data, further research is needed. Our Comment argues that Becht et al. paper does not answer that. (10/10)
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Just to be clear: this is *not* an attack on UMAP! I think UMAP is great :-) But I also think t-SNE is great. And there is plenty of room for further improvements and for better conceptual understanding of this whole family of embedding methods. (9/10)
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This is in agreement with the recommendation to use PCA initialization (rather than random initialization) for t-SNE made in the recent paper by
@CellTypist and me: https://twitter.com/hippopedoid/status/1206535867831083008 …. (8/10)
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