Maria Carilli retweetledi
biVI models the biophysical processes generating nascent and mature single-cell transcriptomes using variational autoencoders.
@MariaCarilli @GorinGennady @funion10 @lpachter
nature.com/articles/s4159…
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Maria Carilli
13 posts
Maria Carilli
@MariaCarilli
🧬 PhD candidate @Caltech in the lab of @lpachter | Computational biology, bioinformatics, biophysics | 🧬
Pasadena, CA Katılım Haziran 2022
105 Takip Edilen140 Takipçiler
Maria Carilli retweetledi
We expanded kallisto to translated alignment (nucleotides <-> amino acids), and used it to detect novel viruses in RNA seq data. The single-cell resolution allowed us to determine whether the presence of these viruses affected host gene expression. 🧵
biorxiv.org/content/10.110…
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@GorinGennady @simocristea @lpachter Actually, we did benchmark against a a non-neural approximation strategy (moment matched the distributions to negative binomials). It was about 10x less accurate, as measured by Hellinger distance, than our neural strategy. How much inferior depends on desired level of accuracy!
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@simocristea @MariaCarilli @lpachter Hard to say, there is nothing to compare it to yet! The big picture is that for a VAE, nothing above O(1) space/time complexity works. Best non-neural solutions are O(n) or so. Perhaps, with some ingenuity, somebody will develop a generic and effective strategy in the future.
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Maria Carilli retweetledi
Interested in "integrating" multimodal #scRNAseq data? W/ @MariaCarilli, @GorinGennady, @funion10 & Tara Chari we introduce biVI, which combines the scVI variational autoencoder with biophysically motivated bivariate models for RNA distributions. 🧵 1/
biorxiv.org/content/10.110…
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@mrazomej @GorinGennady @lpachter Thanks so much! Variational autoencoders are used to find underlying structure in and “de-noise” high-dimensional datasets. Thus, the “integration” of data is achieved by using both spliced and unspliced counts to create one low-dimensional latent embedding of the cell.
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@GorinGennady @lpachter I read the paper this morning and am fascinated with the approach. One question I have (I should probably re-read it) is why a variational autoencoder? Do you use the latent variables, or could you just use any multi-layer percepton to do this? Congrats on the paper!
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Maria Carilli retweetledi
We have another preprint out! Please see @lpachter's thread for an overview from the viewpoint of integration, or read on for
1/ biVI: THE CHEMICAL ENGINEER’S PERSPECTIVE
Lior Pachter@lpachter
Interested in "integrating" multimodal #scRNAseq data? W/ @MariaCarilli, @GorinGennady, @funion10 & Tara Chari we introduce biVI, which combines the scVI variational autoencoder with biophysically motivated bivariate models for RNA distributions. 🧵 1/ biorxiv.org/content/10.110…
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@simocristea @lpachter For non-trivial cases, we in fact do define a set of negative binomial basis functions and use the neural network to learn weights for those functions. While a simpler function could be used to find weights, the approximation could lose accuracy. Details are in the preprint! 2/2
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@simocristea @lpachter Thanks for your question, Simona! There are indeed closed-form solutions to some simple cases. When considering only mature RNA, the bursty model and what we call the "extrinsic model" result in negative binomially distributed counts, and the constitutive in Poisson.
1/
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Maria Carilli retweetledi
gget alphafold: Predict the 3D structure of a protein from its amino acid sequence using @DeepMind’s AlphaFold v2.0 from a Python or command-line environment in 3 lines of code. Runs on any laptop and requires only ~4 GB of disk space. Simply ‘pip install gget’ and:
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Maria Carilli retweetledi
Maria Carilli retweetledi
Spectral neural approximations for models of transcriptional dynamics biorxiv.org/cgi/content/sh… #biorxiv_bioinfo
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my awesome friend has done an awesome thing!
Lior Pachter@lpachter
In a new preprint with @GorinGennady and Maria Carilli (co-first) along with Tara Chari, we show how neural networks can be used to efficiently approximate steady state solutions of two species (nascent and mature RNA) bursty transcription models. biorxiv.org/content/10.110… 🧵1/
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Maria Carilli retweetledi
In a new preprint with @GorinGennady and Maria Carilli (co-first) along with Tara Chari, we show how neural networks can be used to efficiently approximate steady state solutions of two species (nascent and mature RNA) bursty transcription models.
biorxiv.org/content/10.110… 🧵1/
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