Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: springer.com, nature.com, zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.3%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: rohankumardubey
- License: other
- Language: Python
- Default Branch: master
- Size: 5.58 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
-->
<!---
-->
Flameplot - Comparison of (high) dimensional embeddings.
⭐️ Star this repo if you like it ⭐️
Method
To compare the embedding of samples in two different maps, we propose a scale dependent similarity measure. For a pair of maps X and Y, we compare the sets of the, respectively, kx and ky nearest neighbours of each sample. We first define the variable rxij as the rank of the distance of sample j among all samples with respect to sample i, in map X. The nearest neighbor of sample i will have rank 1, the second nearest neighbor rank 2, etc. Analogously, ryij is the rank of sample j with respect to sample i in map Y. Now we define a score on the interval [0, 1], as (eq. 1)
Schematic overview
Schematic overview to systematically compare local and global differences between two sample projections. For illustration we compare two input maps (x and y) in which each map contains n samples (step 1). The second step is the ranking of samples based on Euclidean distance. The ranks of map x are subsequently compared to the ranks of map y for kx and ky nearest neighbours (step 3). The overlap between ranks (step 4), is subsequently summarized in Score: Sx,y(kx,ky).
Functions in flameplot
```python scores = flameplot.compare(map1, map2) fig = flameplot.plot(scores) X,y = flameplot.import_example() fig = flameplot.scatter(Xcoord,Ycoord)
```
Install flameplot from PyPI
bash
pip install flameplot
Import flameplot package
python
import flameplot as flameplot
Documentation pages
On the documentation pages you can find detailed information about the working of the flameplot with examples.
Examples
Support
This project needs some love! ❤️ You can help in various ways.
* Become a Sponsor!
* Star this repo at the github page.
* Other contributions can be in the form of feature requests, idea discussions, reporting bugs, opening pull requests.
* Read more why becoming an sponsor is important on the Sponsor Github Page.
Cheers Mate.
References
- Taskesen, E. et al. Pan-cancer subtyping in a 2D-map shows substructures that are driven by specific combinations of molecular characteristics. Sci. Rep. 6, 24949
- https://static-content.springer.com/esm/art%3A10.1038%2Fsrep24949/MediaObjects/415982016BFsrep24949MOESM12ESM.pdf
- https://www.nature.com/articles/srep24949
Owner
- Name: Rohan Dubey
- Login: rohankumardubey
- Kind: user
- Location: India
- Company: Pokerstars
- Website: https://rohankumardubey.github.io/
- Twitter: rohanku43485614
- Repositories: 1
- Profile: https://github.com/rohankumardubey
if (brain != empty) { keepCoding(); } else { orderCoffee(); }
Citation (CITATION.cff)
# YAML 1.2
---
authors:
-
family-names: Taskesen
given-names: Erdogan
orcid: "https://orcid.org/0000-0002-3430-9618"
cff-version: "1.1.0"
date-released: 2020-01-19
keywords:
- "python"
- "embeddings"
- "pca"
- "tsne"
- "dimensionality-reduction"
- "umap"
- "high-dimensional"
license: "MIT"
message: "If you use this software, please cite it using these metadata."
repository-code: "https://erdogant.github.io/flameplot"
title: "flameplot is a python package for the quantification of local similarity across two maps or embeddings."
version: "1.0.0"
...