Baltica VS awesome-single-cell

Compare Baltica vs awesome-single-cell and see what are their differences.

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Baltica awesome-single-cell
1 3
7 2,950
- -
2.6 5.2
about 1 year ago 2 months ago
R
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

Baltica

Posts with mentions or reviews of Baltica. We have used some of these posts to build our list of alternatives and similar projects.
  • Novel Splice Variants in RNAseq Data
    1 project | /r/bioinformatics | 15 Sep 2021
    Also, we've been working on a framework with multiple workflows for differential splicing: Baltica: integrated differential junction usage (DJU) https://github.com/dieterich-lab/Baltica . Please have on mind that Baltica is work in progress, but the workflows are quite mature. Fell free to contact me if you have any questions!

awesome-single-cell

Posts with mentions or reviews of awesome-single-cell. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-08-21.

What are some alternatives?

When comparing Baltica and awesome-single-cell you can also consider the following projects:

rhmmer - Simple R utilities for working with HMMER

cellsnp-lite - Efficient genotyping bi-allelic SNPs on single cells

drugfindR - Repository holding the code for the drugfindR R package

MultiQC - Aggregate results from bioinformatics analyses across many samples into a single report.

ermineR - R wrapper for ermineJ

Awesome-Bioinformatics - A curated list of awesome Bioinformatics libraries and software.

scDblFinder - Methods for detecting doublets in single-cell sequencing data

DGE_workshop