future
seurat
| future | seurat | |
|---|---|---|
| 2 | 13 | |
| 932 | 2,125 | |
| - | 1.9% | |
| 8.1 | 9.6 | |
| 14 days ago | 22 days ago | |
| R | R | |
| - | GNU General Public License v3.0 or later |
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future
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Running Code in Parallel
Check out the future package: https://github.com/HenrikBengtsson/future
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What's faster: run simultaneously on multiple terminals, or run everything sequentially on one terminal?
If you're a fan of the tidyverse check out the furrr package, which is based on the future package. Let's you apply your map() functions in parallel very easily.
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
What are some alternatives?
R-sharp - R# language is a kind of R liked vectorized language implements on .NET environment for the bioinformatics data analysis
scvi-tools - Deep probabilistic analysis of single-cell and spatial omics data
HoRM - Supplemental Functions and Datasets for "Handbook of Regression Methods"
pagoda2 - R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
hts - Hierarchical and Grouped Time Series
frontends-team-compass - A repository for team interaction, syncing, and handling meeting notes across the JupyterLab ecosystem.
r2u - CRAN as Ubuntu Binaries
popscle - A suite of population scale analysis tools for single-cell genomics data including implementation of Demuxlet / Freemuxlet methods and auxilary tools
openxlsx - openxlsx - a fast way to read and write complex xslx files
MAST - Tools and methods for analysis of single cell assay data in R
parsel - parallel execution of RSelenium
packagefinder - Comfortable search for R packages on CRAN, either directly from the R console or with an R Studio add-in