seurat
scvi-tools
| seurat | scvi-tools | |
|---|---|---|
| 13 | 2 | |
| 2,129 | 1,135 | |
| 2.1% | 2.9% | |
| 9.6 | 9.4 | |
| 6 days ago | 3 days ago | |
| R | Python | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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seurat
- Help with spatial transcriptomic analysis
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Seurat installation issues on macbook (for RNA-seq analysis)
So there is an issue with the ModularityOptimizer? I looked it up and it comes from this script: https://github.com/satijalab/seurat/blob/master/src/RModularityOptimizer.cpp
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Does Seurat provide any advantage for UMAPs over other packages (e.g. uwot)?
With regards to using selected features for UMAP, my gut tells me it's possible, but it looks like the folks here were having some issues getting it work. Your mileage may vary.
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Weird Phishing attempt on GitHub
The strangest thing is that I'm not even a follower of this repo where the discussion was started, which by the way is a R toolkit for single cell genomics.
- I feel like nobody knows anything about what they're doing (including me) and it's making me think twice about working in bioinformatics. Is it a bioinformatics problem or is it a lab problem?
- Use of Seurat integrated assay
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Extract Log-Normalised Data From a Seurat Object
There is a good wiki of the Seurat data object and information about the slots and objects can be found here: https://github.com/satijalab/seurat/wiki
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Reference request: single cell RNA seq papers where cells originate from multiple individuals where the individual of origin was explicitly accounted for in the model?
Here is link number 1 - Previous text "1"
- Seurat
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Is there a way to obtain the raw source codes of Seurat functions?
Wow, it's not easy to find, indeed. They should do a better job commenting the code, and documenting the code organisation. But it's all in the Github repo, for example NormalizeData is defined here: https://github.com/satijalab/seurat/blob/4e868fcde49dc0a3df47f94f5fb54a421bfdf7bc/R/generics.R#L337
scvi-tools
- Error with scvi
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python for bioinformatics
I use both but I prefere python, its much easier for huge datasets, and a lot of useful tools are only available on python: https://github.com/YosefLab/scvi-tools
What are some alternatives?
pagoda2 - R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
disentangling-vae - Experiments for understanding disentanglement in VAE latent representations
frontends-team-compass - A repository for team interaction, syncing, and handling meeting notes across the JupyterLab ecosystem.
benchmark_VAE - Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone
DALLE-mtf - Open-AI's DALL-E for large scale training in mesh-tensorflow.
popscle - A suite of population scale analysis tools for single-cell genomics data including implementation of Demuxlet / Freemuxlet methods and auxilary tools
sanbomics_scripts - scripts and notebooks from sanbomics
MAST - Tools and methods for analysis of single cell assay data in R
Roundtrip - Roundtrip: density estimation with deep generative neural networks
packagefinder - Comfortable search for R packages on CRAN, either directly from the R console or with an R Studio add-in
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.