This is the Windows app named awesome-single-cell whose latest release can be downloaded as 2026-02-02sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named awesome-single-cell with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
awesome-single-cell
DESCRIPTION
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc. List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. Rapid, accurate and memory-frugal preprocessing of single-cell and single-nucleus RNA-seq data. Find bimodal, unimodal, and multimodal features in your data. Ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. An analytical framework for big-scale single cell data. Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction.
Features
- Find bimodal, unimodal, and multimodal features in your data
- An analytical framework for big-scale single cell data
- Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model
- Representation Learning for detection of phenotype-associated cell subsets
- Bayesian pseudotime estimation algorithms
- Basic PCA-based workflow for analysis and plotting of single cell RNA-seq data
- And more
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/awesome-single-cell.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
