seurat
ck
| seurat | ck | |
|---|---|---|
| 13 | 9 | |
| 2,129 | 580 | |
| 2.1% | 1.2% | |
| 9.6 | 10.0 | |
| 6 days ago | 6 days ago | |
| R | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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
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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
ck
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Do you have an idle @Nvidia GPU? Can you please help the community test the beta version of the open-source framework for composable benchmarking and design space exploration of ML Systems?
If you have an idle Nvidia GPU and Linux, can you please help the community test the beta version of the open-source framework for composable benchmarking and design space exploration of ML systems: https://github.com/mlcommons/ck/blob/master/cm-mlops/project/mlperf-inference-v3.0-submissions/docs/crowd-benchmark-mlperf-bert-inference-cuda.md ?
- Sharing a tutorial to modularize ML Systems
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[N] Tutorial to modularize ML Systems benchmarks from the Student Cluster Competition'22
Hi! Just sharing this tutorial from the Student Cluster Competition at SuperComputing'22 to learn how to modularize and run ML Systems benchmarks. 10 international teams had about 30 minutes to run it and most of them succeeded while sharing their results at the live dashboard . It is a part of the ongoing effort to modularize ML Systems and automate their benchmarking and optimization. Feedback is very welcome!
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Asking for a favor to test modular ML benchmark for Student Cluster Competition
We would like to ask for a favor: we have prepared a tutorial to help students run the MLPerf inference benchmark across different platforms at the Student Cluster Competition at SuperComputing'22 in a few days: https://github.com/mlcommons/ck/blob/master/docs/tutorials/s... .
We would like to test it across different machines before students run it ;) . If you have time, please help us go through this tutorial and run this benchmark on any available system - it should not take more than 20..30 minutes.
If you encounter any issues, please report them at https://github.com/mlcommons/ck/issues so that we could fix them before the competition.
Thank you for supporting this community project!
- MLCommons is creating a new working group to modularize ML Systems
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[N] Open working group to modularize ML Systems
Just to let you know that we are preparing a new working group at MLCommons to help the community modularize ML/AI Systems and automate their benchmarking, optimization and deployment. It will be based on the MLPerf methodology and MLCommons "Collective Knowledge" automation meta-framework that was already used to automate recent MLPerf inference benchmark submissions from Qualcomm, HPE, Lenovo, Krai, DELL and OctoML. Please join the group here to provide your feedback and help with this community effort! Thank you!
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[N] Releasing the MLPerf automation framework to plug in real-world ML models, data sets and tools
Hi! Just sharing our open-source project to automate MLPerf benchmarks and make it easier for everyone to plug in their real-world ML models, data sets, frameworks/SDKs and hardware. Feedback is very welcome!
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Research software code is likely to remain a tangled mess
– Their solution product https://cknowledge.io/ and source code https://github.com/ctuning/ck\
I guess it should be helpful to the researchers community.
What are some alternatives?
scvi-tools - Deep probabilistic analysis of single-cell and spatial omics data
osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.
pagoda2 - R package for analyzing and interactively exploring large-scale single-cell RNA-seq datasets
SmartSim - SmartSim Infrastructure Library.
frontends-team-compass - A repository for team interaction, syncing, and handling meeting notes across the JupyterLab ecosystem.
budgetml - Deploy a ML inference service on a budget in less than 10 lines of code.
future - :rocket: R package: future: Unified Parallel and Distributed Processing in R for Everyone
dslinter - `dslinter` is a pylint plugin for linting data science and machine learning code. We plan to support the following Python libraries: TensorFlow, PyTorch, Scikit-Learn, Pandas and NumPy.
popscle - A suite of population scale analysis tools for single-cell genomics data including implementation of Demuxlet / Freemuxlet methods and auxilary tools
MAST - Tools and methods for analysis of single cell assay data in R
aws-deployment-framework - The AWS Deployment Framework (ADF) is an extensive and flexible framework to manage and deploy resources across multiple AWS accounts and regions based on AWS Organizations.