seurat
future
| seurat | future | |
|---|---|---|
| 13 | 2 | |
| 2,129 | 933 | |
| 2.1% | - | |
| 9.6 | 8.1 | |
| 6 days ago | 25 days ago | |
| R | R | |
| GNU General Public License v3.0 or later | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
seurat
- Help with spatial transcriptomic analysis
-
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
-
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.
-
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
-
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
-
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
-
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
future
-
Running Code in Parallel
Check out the future package: https://github.com/HenrikBengtsson/future
-
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.
What are some alternatives?
scvi-tools - Deep probabilistic analysis of single-cell and spatial omics data
R-sharp - R# language is a kind of R liked vectorized language implements on .NET environment for the bioinformatics data analysis
pagoda2 - R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
HoRM - Supplemental Functions and Datasets for "Handbook of Regression Methods"
frontends-team-compass - A repository for team interaction, syncing, and handling meeting notes across the JupyterLab ecosystem.
hts - Hierarchical and Grouped Time Series
popscle - A suite of population scale analysis tools for single-cell genomics data including implementation of Demuxlet / Freemuxlet methods and auxilary tools
r2u - CRAN as Ubuntu Binaries
MAST - Tools and methods for analysis of single cell assay data in R
openxlsx - openxlsx - a fast way to read and write complex xslx files
packagefinder - Comfortable search for R packages on CRAN, either directly from the R console or with an R Studio add-in
parsel - parallel execution of RSelenium