liger | R package | Genomics library

by welch-lab R Version: v1.1.0 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | liger Summary

liger is a R library typically used in Artificial Intelligence, Genomics applications. liger has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

LIGER (installed as rliger ) is a package for integrating and analyzing multiple single-cell datasets, developed by the Macosko lab and maintained/extended by the Welch lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors. Check out our Cell paper for a more complete description of the methods and analyses. To access data used in our SN and BNST analyses, visit our study on the Single Cell Portal.

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Quality

Security

License

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Support

liger has a low active ecosystem.

It has 283 star(s) with 76 fork(s). There are 22 watchers for this library.

It had no major release in the last 12 months.

There are 31 open issues and 134 have been closed. On average issues are closed in 176 days. There are 12 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of liger is v1.1.0

Quality

liger has 0 bugs and 0 code smells.

Security

liger has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

liger code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

liger is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

liger releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 3722 lines of code, 0 functions and 12 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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liger Key Features

No Key Features are available at this moment for liger.

liger Examples and Code Snippets

No Code Snippets are available at this moment for liger.

Community Discussions

Trending Discussions on liger

JavaScript/HTML Word Guessing Game: difficulty levels w/drop-down

QUESTION

JavaScript/HTML Word Guessing Game: difficulty levels w/drop-down

Asked 2021-Dec-02 at 00:06

I am working on a project to have a random word guessing game. So far most of the code is working but I am trying to implement some rules on the length of words displayed to the user as a measure of game difficulty (shorter words = easier, etc). I am using a drop-down menu to get the user's setting selection, and then have rules in the JS tags that are supposed to be handling this.

After toying around with this for several days, I was hoping that a fresh pair of eyes might have a suggestion about where I am going wrong to be able to enforce the rules I am trying to enforce?

The specific functions that should be handling this are setDifficulty(), getSelection(), and randomWord()

...

ANSWER

Answered 2021-Dec-02 at 00:06

Let's start by saving the difficulty setting in a variable along these :

Source https://stackoverflow.com/questions/70192625

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install liger

LIGER is written in R and is also available on the Comprehensive R Archive Network (CRAN). Note that the package name is rliger to avoid a naming conflict with an unrelated package. To install the version on CRAN, follow these instructions:.
Install R (>= 3.4)
Install Rstudio (recommended)
Type the following R command:
The HDF5 library is required for implementing online learning in LIGER on data files in HDF5 format. It can be installed via one of the following commands:. For Windows, the latest HDF5 1.12.0 is available at https://www.hdfgroup.org/downloads/hdf5/.
The installation process of rliger should take less than 30 minutes. The expected run time is 1 - 4 hours depending on dataset size and downstream analysis of the user’s choice.