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https://github.com/theislab/paga

Mapping out the coarse-grained connectivity structures of complex manifolds.
https://github.com/theislab/paga

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Mapping out the coarse-grained connectivity structures of complex manifolds.

Host: GitHub
URL: https://github.com/theislab/paga
Owner: theislab
License: bsd-3-clause
Created: 2018-06-08T01:53:10.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2020-05-23T04:57:00.000Z (about 4 years ago)
Last Synced: 2023-10-20T23:27:22.660Z (8 months ago)
Language: Jupyter Notebook
Homepage:
Size: 28.7 MB
Stars: 170
Watchers: 10
Forks: 29
Open Issues: 21
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

awesome-biomedical-machine-learning - paga: Mapping out the coarse-grained connectivity structures of complex manifolds.

README

        # PAGA - partition-based graph abstraction

*Mapping out the coarse-grained connectivity structures of complex manifolds [(Genome Biology, 2019)](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1663-x).*

![PAGA for hematopoiesis.](http://www.falexwolf.de/img/paga_paul15.png "PAGA for hematopoiesis.")

PAGA is available within [Scanpy](https://scanpy.readthedocs.io/en/latest/examples.html#trajectory-inference) through: [`tl.paga`](https://scanpy.readthedocs.io/en/latest/api/scanpy.tl.paga.html) | [`pl.paga`](https://scanpy.readthedocs.io/en/latest/api/scanpy.pl.paga.html) | [`pl.paga_path`](https://scanpy.readthedocs.io/en/latest/api/scanpy.pl.paga_path.html) | [`pl.paga_compare`](https://scanpy.readthedocs.io/en/latest/api/scanpy.pl.paga_compare.html).

Below you find links to all central example notebooks, which also allow reproducing all main figures of the paper. If you start working with PAGA, go through [*blood/paul15*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/paul15/paul15.ipynb).

notebook       | system         | details  | reference | figure

---------------| ---------------| ---------| ----------| ------

[*blood/simulated*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/simulated/simulated.ipynb) | hematopoiesis | simulated | [Krumsiek *et al.*, Plos One (2011)](https://doi.org/10.1371/journal.pone.0022649) | 2a

[*blood/paul15*](https://scanpy-tutorials.readthedocs.io/en/latest/paga-paul15.html) | murine hematopoiesis | 2,730 cells, MARS-seq | [Paul *et al.*, Cell (2015)](https://doi.org/10.1016/j.cell.2015.11.013) | 2b

[*blood/nestorowa16*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/nestorowa16/nestorowa16.ipynb) | murine hematopoiesis | 1,654 cells, Smart-seq2 | [Nestorowa *et al.*, Blood (2016)](https://doi.org/10.1182/blood-2016-05-716480) | 2c

[*blood/dahlin18*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/dahlin18/dahlin18.ipynb) | murine hematopoiesis | 44,802 cells, 10x Genomics | [Dahlin *et al.*, Blood (2018)](https://doi.org/10.1182/blood-2017-12-821413) | 2d

[*planaria*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/planaria/planaria.ipynb) | planaria | 21,612 cells | [Plass *et al.*, Science (2018)](https://doi.org/10.1126/science.aaq1723) | 3

[*zebrafish*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/zebrafish/zebrafish.ipynb) | zebrafish embryo | 53,181 cells |  [Wagner *et al.*, Science (2018)](https://doi.org/10.1126/science.aar4362) | 4

[*1M_neurons*](https://github.com/theislab/scanpy_usage/blob/master/170522_visualizing_one_million_cells/logfile_1.3M.txt) | neurons | 1.3 million cells, 10x Genomics | [10x Genomics (2017)](https://support.10xgenomics.com/single-cell-gene-expression/datasets/1M_neurons) | S12

[*deep_learning*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/deep_learning/deep_learning.ipynb) | cycling Jurkat cells | 30,000 single-cell images |  [Eulenberg *et al.*, Nat. Commun. (2017)](https://doi.org/10.1038/s41467-017-00623-3) | S14

All supplemental figures of the paper can be reproduced based on the following table.

notebook       |  description | figure

---------------|  ----------| ------

[*connectivity_measure*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/connectivity_measure/connectivity_measure.ipynb) | connectivity measure | S1, S2, S3

[*robustness*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/robustness) | robustness and multi-resolution capacity | S4, S5

[*comparisons/simulated_data*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/comparisons/simulated_data) | comparisons for simulated data | S6, S7

[*comparisons/paul15_monocle2*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/comparisons/paul15_monocle2) | comparison Monocle 2 for Paul *et al.* (2015) | S8

[*comparisons/nestorowa16_monocle2*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/comparisons/nestorowa16_monocle2) | comparison Monocle 2 for Nestorowa *et al.* (2016) | S9

[*embedding_quality*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/embedding_quality/embedding_quality.ipynb) | quantifying embedding quality | S10

[*simulation*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/simulated/simulated.ipynb) | simulating hematopoiesis | S11

[*1M_neurons*](https://github.com/theislab/scanpy_usage/blob/master/170522_visualizing_one_million_cells/logfile_1.3M.txt) | neurons, 1.3 million cells, 10x Genomics, [10x Genomics (2017)](https://support.10xgenomics.com/single-cell-gene-expression/datasets/1M_neurons) | S12

[*blood/paul15*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/blood/paul15/paul15.ipynb) | annotation of louvain clusters using PAGA | S13

[*deep_learning*](https://nbviewer.jupyter.org/github/theislab/paga/blob/master/deep_learning/deep_learning.ipynb) | cycling Jurkat cells, 30,000 single-cell images, [Eulenberg *et al.*, Nat. Commun. (2017)](https://doi.org/10.1038/s41467-017-00623-3) | S14