| Name | blitzgsea JSON |
| Version |
1.3.39
JSON |
| download |
| home_page | https://github.com/maayanlab/blitzgsea |
| Summary | Package for fast and accurate calculation of Gene Set Enrichment Analysis (GSEA) similar to prerank using gamma distribution approximation. |
| upload_time | 2024-02-23 02:49:01 |
| maintainer | |
| docs_url | None |
| author | Alexander Lachmann |
| requires_python | >=3.6 |
| license | |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img title="a title" alt="blitzGSEA" src="https://github.com/MaayanLab/blitzgsea/raw/main/icon/bgsea_small.png" width=200>
[Installation](#installation) | [Example](#python-example) | [Optional Parameters](#optional-parameters) | [Speed-up](#speeding-up-enrichment-calculations) | [Plotting](#plotting-enrichment-results) | [Attribution](#attribution) | [References](#references)
# blitzGSEA Introduction
This Python package provides a computationally performant <b>G</b>ene <b>S</b>et <b>E</b>nrichment <b>A</b>nalysis (GSEA) implementation of the pre-rank algorithm [1]. GSEApy was used as the reference for the running sum and enrichment score calculation [2]. The algorithm estimates the enrichment score (ES) distribution of the null model by fitting data to gamma distibutions instead of calculating permutations for each gene set. blitzGSEA calculates p-values with much higher accuracy than other reference implementations available in Python.
Gene set libraries can directly be loaded from Enrichr (<a href="https://maayanlab.cloud/Enrichr" target="_blank">https://maayanlab.cloud/Enrichr</a>). For this use the `blitzgsea.enrichr.get_library()` function. All libraries can also be listed with `blitzgsea.enrichr.print_libraries()`.
blitzGSEA provides plotting functions to generate publication ready figures similar to the original GSEA-P software. `blitzgsea.plot.running_sum()` plots an enrichment plot for a single gene set and `blitzgsea.plot.top_table()` plots the top `n` gene sets in a compact table.
# Installation
<span id="#installation"></span>
blitzGSEA is currently only available as a Python package in this GitHub repository. You can install the blitzGSEA Python package and its dependencies through pip by using the following command:
```
$ pip install blitzgsea
```
# Run enrichment analysis using blitzGSEA
blitzGSEA depends on two input files. 1) a gene signature and 2) a gene set library. The gene set library is a dictionary with the name of the gene set as key and a list of gene ids as values. Gene set libraries can be loaded directly from Enrichr. The signature should be a pandas dataframe with two columns [0,1]. The first column should contain the gene ids (matching the gene ids in the gene set library).
### Python example
This short example will download two files (signature and gene set library). The gene set library consists of KEGG pathways and the signature is an example signature of differential gene expression of muscle samples from young and old donors. Differential gene expression was computed with Limma Voom.
```python
import blitzgsea as blitz
import pandas as pd
# read signature as pandas dataframe
signature = pd.read_csv("https://github.com/MaayanLab/blitzgsea/raw/main/testing/ageing_muscle_gtex.tsv")
# list available gene set libraries in Enrichr
blitz.enrichr.print_libraries()
# use enrichr submodule to retrieve gene set library
library = blitz.enrichr.get_library("KEGG_2021_Human")
# run enrichment analysis
result = blitz.gsea(signature, library)
```
### Example Input
| index | 0 | 1 |
|:-----|:-------------:|------:|
| 1 | ADO | -7.833439 |
| 2 | CHUK | -7.800920 |
| 3 | GOLGA4 | -7.78722 |
| ... | ... | ... |
The gene set library is a dictionary with the gene set names as key and lists of gene ids as values.
```python
{
'ERBB2 SIGNALING PATHWAY (GO:0038128)': ['CDC37',
'PTPN12',
'PIK3CA',
'SOS1',
'CPNE3',
'EGF',
...
],
'HETEROTYPIC CELL-CELL ADHESION (GO:0034113)': ['DSC2',
'ITGAV',
'ITGAD',
'LILRB2',
...
],
...
}
```
### Optional Parameters
The main function of `blitzgsea.gsea()` supports several optional parameters. The default parameters should work well for most use cases.
| parameter name | type | default | description |
|:-----|:---------|:-------------|:------|
| `permutations` | int | 2000 | Number of randomized permutations to estimate ES distributions. |
| `min_size` | int | 5 | Minimum number of genes in geneset. |
| `max_size` | int | 4000 | Maximal number of genes in gene set. |
| `anchors` | int | 20 | Number of gene set size distributions calculated. Remaining are interpolated. |
| `processes` | int | 4 | Number of parallel threads. Not much gain after 4 threads. |
| `symmetric` | bool | False | Use same distribution parameters for negative and positive ES. If `False` estimate them separately. |
| `signature_cache` | bool | True | Cache precomputed anchor parameters in memory for later reuse. |
| `shared_null` | bool | False | Use same null for signatures if a compatible model already exists. (uses KL-divergence test). |
| `kl_threshold`| float | 0.3 | Controls how similar signature value distributions have to be for reuse. |
| `kl_bins`| int | 200 | Number of bins in PDF representation of distributions for KL test. |
| `plotting`| bool | False | Plot estimated anchor parametes. |
| `verbose` | bool | False | Toggle additonal output. |
| `progress` | bool | False | Toggle progress bar. |
| `seed` | int | 0 | Random seed. Same seed will result in identical result. If seed equal `-1` generate random seed. |
| `add_noise` | bool | False | Add small random noise to signature. The noise is a fraction of the expression values. |
| `center` | bool | True | Center signature values at 0 before calculating running sum. |
### Speeding up enrichment calculations
blitzGSEA is currently the fastest GSEA implementation. The most time-consuming step of blitzGSEA is the generation of a robust null distribution to compute p-values. Since the null distribution depends on the value distribution of the input signature, blitzGSEA will, by default, compute a new null for each new input signature. blitzGSEA can compute the similarity between input signatures using Kullback–Leibler divergence to identify similar signatures to share null models. A cached null model is used if a previous signature has a similar value distribution. The relevant parameters of the `blitzgsea.gsea()` function are shown below:
| parameter name | type | default | description |
|:-----|:---------|:-------------|:------|
| `signature_cache` | bool | True | Cache precomputed anchor parameters in memory for later reuse. |
| `shared_null` | bool | False | Use same null for signatures if a compatible model already exists. (uses KL-divergence test). |
| `kl_threshold`| float | 0.3 | Controls how similar signature value distributions have to be for reuse. The smaller the more conservative. |
| `kl_bins`| int | 200 | Number of bins in PDF representation of distributions for KL test. |
### Example
```python
import blitzgsea as blitz
import pandas as pd
# read signature as pandas dataframe
signature = pd.read_csv("https://github.com/MaayanLab/blitzgsea/raw/main/testing/ageing_muscle_gtex.tsv")
# run enrichment analysis
result = blitz.gsea(signature, library, shared_null=True)
```
### Plotting enrichment results
blitzGSEA supports several plotting functions. `blitzgsea.plot.running_sum()` and `blitzgsea.plot.top_table()` can be used after enrichment has been performed. `blitzgsea.plot.running_sum()` shows the running sum of an individual gene set. It has a `compact` mode in which the image will be more readable if small. `blitzgsea.plot.top_table()` shows the top `n` enriched gene sets and displays the results in a table, with normalized enrichment score (NES) and the distribution of hits relative to the gene ranking of the signature.
### Example
```python
import blitzgsea as blitz
import pandas as pd
# read signature as pandas dataframe
signature = pd.read_csv("https://github.com/MaayanLab/blitzgsea/raw/main/testing/ageing_muscle_gtex.tsv")
# use enrichr submodule to retrieve gene set library
library = blitz.enrichr.get_library("KEGG_2021_Human")
# run enrichment analysis
result = blitz.gsea(signature, library)
# plot the enrichment results and save to pdf
fig = blitz.plot.running_sum(signature, "Cell adhesion molecules", library, result=result, compact=False)
fig.savefig("running_sum.png", bbox_inches='tight')
fig_compact = blitz.plot.running_sum(signature, "Cell adhesion molecules", library, result=result, compact=True)
fig_compact.savefig("running_sum_compact.png", bbox_inches='tight')
fig_table = blitz.plot.top_table(signature, library, result, n=15)
fig_table.savefig("top_table.png", bbox_inches='tight')
# disable switching of interactive plotting, which can cause issues in jupyter notebooks
# will plot the figure into the notebook
fig = blitz.plot.running_sum(signature, "Cell adhesion molecules", library, result=result, compact=False, interactive_plot=True)
fig_table = blitz.plot.top_table(signature, library, result, n=15, interactive_plot=True)
```
The resulting plots will look like the examples below:
#### running_sum.pdf
<div style="bachground-color: white">
<img title="a title" alt="blitzGSEA sunning_sum" src="https://github.com/MaayanLab/blitzgsea/raw/main/icon/running_sum.png" width=300>
</div>
#### running_sum_compact.pdf
<img title="a title" alt="blitzGSEA sunning_sum" src="https://github.com/MaayanLab/blitzgsea/raw/main/icon/running_sum_compact.png" width=300>
#### top_table.pdf
<img title="a title" alt="blitzGSEA sunning_sum" src="https://github.com/MaayanLab/blitzgsea/raw/main/icon/top_table.png" width=300>
### Sample shuffling
This is the sample shuffling algorithm from GSEApy. It performs a t-test to build signatures for phenotype shuffled groups. The input is a gene expression dataframe, which should be normalized for library size. `groups` is a list containing 0 or 1 describing the corresponding group for the samples in `exprs`. The index of `exprs` are the gene ids matching the gene set library.
```python
blitz.shuffle.gsea(exprs, library, groups, permutations=50, seed=1)
```
| parameter name | type | default | description |
|:-----|:---------|:-------------|:------|
| `exprs` | pd.DataFrame | NA | Normalized gene expression matrix. |
| `library` | dictionary | NA | Gene set library. |
| `groups` | list | NA | Phenotype group labels of samples. Labels are 0 or 1. |
| `permutations` | int | 1000 | Number of permutations. |
| `seed` | int | 1 | Random state seed. |
# Dependencies
Python 3.6+
# Attribution
The statistical tool was developed by the [Ma'ayan Laboratory](https://labs.icahn.mssm.edu/maayanlab/). When using blitzgsea please cite the following reference:
Lachmann, Alexander, Zhuorui Xie, and Avi Ma’ayan. "blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation." Bioinformatics (2022).
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac076/6526383?login=false
# References
[1] Lachmann, Alexander, Zhuorui Xie, and Avi Ma’ayan. "blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation." Bioinformatics (2022).
[2] Subramanian, Aravind, Heidi Kuehn, Joshua Gould, Pablo Tamayo, and Jill P. Mesirov. "GSEA-P: a desktop application for Gene Set Enrichment Analysis." Bioinformatics 23, no. 23 (2007): 3251-3253.
[3] Fang, Zhuoqing, Xinyuan Liu, and Gary Peltz. "GSEApy: a comprehensive package for performing gene set enrichment analysis in Python." Bioinformatics (2022).
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"description": "<img title=\"a title\" alt=\"blitzGSEA\" src=\"https://github.com/MaayanLab/blitzgsea/raw/main/icon/bgsea_small.png\" width=200>\n\n[Installation](#installation) | [Example](#python-example) | [Optional Parameters](#optional-parameters) | [Speed-up](#speeding-up-enrichment-calculations) | [Plotting](#plotting-enrichment-results) | [Attribution](#attribution) | [References](#references)\n\n# blitzGSEA Introduction\n\nThis Python package provides a computationally performant <b>G</b>ene <b>S</b>et <b>E</b>nrichment <b>A</b>nalysis (GSEA) implementation of the pre-rank algorithm [1]. GSEApy was used as the reference for the running sum and enrichment score calculation [2]. The algorithm estimates the enrichment score (ES) distribution of the null model by fitting data to gamma distibutions instead of calculating permutations for each gene set. blitzGSEA calculates p-values with much higher accuracy than other reference implementations available in Python.\n\nGene set libraries can directly be loaded from Enrichr (<a href=\"https://maayanlab.cloud/Enrichr\" target=\"_blank\">https://maayanlab.cloud/Enrichr</a>). For this use the `blitzgsea.enrichr.get_library()` function. All libraries can also be listed with `blitzgsea.enrichr.print_libraries()`.\n\nblitzGSEA provides plotting functions to generate publication ready figures similar to the original GSEA-P software. `blitzgsea.plot.running_sum()` plots an enrichment plot for a single gene set and `blitzgsea.plot.top_table()` plots the top `n` gene sets in a compact table. \n\n# Installation\n<span id=\"#installation\"></span>\nblitzGSEA is currently only available as a Python package in this GitHub repository. You can install the blitzGSEA Python package and its dependencies through pip by using the following command:\n\n```\n$ pip install blitzgsea\n```\n\n# Run enrichment analysis using blitzGSEA\n\nblitzGSEA depends on two input files. 1) a gene signature and 2) a gene set library. The gene set library is a dictionary with the name of the gene set as key and a list of gene ids as values. Gene set libraries can be loaded directly from Enrichr. The signature should be a pandas dataframe with two columns [0,1]. The first column should contain the gene ids (matching the gene ids in the gene set library).\n\n### Python example\n\nThis short example will download two files (signature and gene set library). The gene set library consists of KEGG pathways and the signature is an example signature of differential gene expression of muscle samples from young and old donors. Differential gene expression was computed with Limma Voom.\n\n```python\nimport blitzgsea as blitz\nimport pandas as pd\n\n# read signature as pandas dataframe\nsignature = pd.read_csv(\"https://github.com/MaayanLab/blitzgsea/raw/main/testing/ageing_muscle_gtex.tsv\")\n\n# list available gene set libraries in Enrichr\nblitz.enrichr.print_libraries()\n\n# use enrichr submodule to retrieve gene set library\nlibrary = blitz.enrichr.get_library(\"KEGG_2021_Human\")\n\n# run enrichment analysis\nresult = blitz.gsea(signature, library)\n```\n\n### Example Input\n\n| index | 0\t| 1 |\n|:-----|:-------------:|------:|\n| 1\t| ADO\t| -7.833439 |\n| 2\t| CHUK\t| -7.800920 |\n| 3\t| GOLGA4\t| -7.78722 |\n| ... | ... | ... |\n\nThe gene set library is a dictionary with the gene set names as key and lists of gene ids as values.\n\n```python\n{\n'ERBB2 SIGNALING PATHWAY (GO:0038128)': ['CDC37',\n 'PTPN12',\n 'PIK3CA',\n 'SOS1',\n 'CPNE3',\n 'EGF',\n ...\n ],\n'HETEROTYPIC CELL-CELL ADHESION (GO:0034113)': ['DSC2',\n 'ITGAV',\n 'ITGAD',\n 'LILRB2',\n ...\n ],\n...\n}\n```\n\n### Optional Parameters\n\nThe main function of `blitzgsea.gsea()` supports several optional parameters. The default parameters should work well for most use cases. \n\n| parameter name | type | default\t| description |\n|:-----|:---------|:-------------|:------|\n| `permutations`\t| int | 2000\t| Number of randomized permutations to estimate ES distributions. |\n| `min_size` | int | 5 | Minimum number of genes in geneset. |\n| `max_size` | int | 4000 | Maximal number of genes in gene set. |\n| `anchors`\t| int | 20 | Number of gene set size distributions calculated. Remaining are interpolated. |\n| `processes`\t| int | 4\t| Number of parallel threads. Not much gain after 4 threads. |\n| `symmetric` | bool | False | Use same distribution parameters for negative and positive ES. If `False` estimate them separately. |\n| `signature_cache` | bool | True | Cache precomputed anchor parameters in memory for later reuse. |\n| `shared_null` | bool | False | Use same null for signatures if a compatible model already exists. (uses KL-divergence test). |\n| `kl_threshold`| float | 0.3 | Controls how similar signature value distributions have to be for reuse. |\n| `kl_bins`| int | 200 | Number of bins in PDF representation of distributions for KL test. |\n| `plotting`| bool | False | Plot estimated anchor parametes. |\n| `verbose` | bool | False | Toggle additonal output. |\n| `progress` | bool | False | Toggle progress bar. |\n| `seed` | int | 0 | Random seed. Same seed will result in identical result. If seed equal `-1` generate random seed. |\n| `add_noise` | bool | False | Add small random noise to signature. The noise is a fraction of the expression values. |\n| `center` | bool | True | Center signature values at 0 before calculating running sum. |\n\n### Speeding up enrichment calculations\n\nblitzGSEA is currently the fastest GSEA implementation. The most time-consuming step of blitzGSEA is the generation of a robust null distribution to compute p-values. Since the null distribution depends on the value distribution of the input signature, blitzGSEA will, by default, compute a new null for each new input signature. blitzGSEA can compute the similarity between input signatures using Kullback\u2013Leibler divergence to identify similar signatures to share null models. A cached null model is used if a previous signature has a similar value distribution. The relevant parameters of the `blitzgsea.gsea()` function are shown below:\n\n| parameter name | type | default\t| description |\n|:-----|:---------|:-------------|:------|\n| `signature_cache` | bool | True | Cache precomputed anchor parameters in memory for later reuse. |\n| `shared_null` | bool | False | Use same null for signatures if a compatible model already exists. (uses KL-divergence test). |\n| `kl_threshold`| float | 0.3 | Controls how similar signature value distributions have to be for reuse. The smaller the more conservative. |\n| `kl_bins`| int | 200 | Number of bins in PDF representation of distributions for KL test. |\n\n### Example\n```python\n\nimport blitzgsea as blitz\nimport pandas as pd\n\n# read signature as pandas dataframe\nsignature = pd.read_csv(\"https://github.com/MaayanLab/blitzgsea/raw/main/testing/ageing_muscle_gtex.tsv\")\n\n# run enrichment analysis\nresult = blitz.gsea(signature, library, shared_null=True)\n```\n\n### Plotting enrichment results\n\nblitzGSEA supports several plotting functions. `blitzgsea.plot.running_sum()` and `blitzgsea.plot.top_table()` can be used after enrichment has been performed. `blitzgsea.plot.running_sum()` shows the running sum of an individual gene set. It has a `compact` mode in which the image will be more readable if small. `blitzgsea.plot.top_table()` shows the top `n` enriched gene sets and displays the results in a table, with normalized enrichment score (NES) and the distribution of hits relative to the gene ranking of the signature.\n\n### Example\n```python\n\nimport blitzgsea as blitz\nimport pandas as pd\n\n# read signature as pandas dataframe\nsignature = pd.read_csv(\"https://github.com/MaayanLab/blitzgsea/raw/main/testing/ageing_muscle_gtex.tsv\")\n\n# use enrichr submodule to retrieve gene set library\nlibrary = blitz.enrichr.get_library(\"KEGG_2021_Human\")\n\n# run enrichment analysis\nresult = blitz.gsea(signature, library)\n\n# plot the enrichment results and save to pdf\nfig = blitz.plot.running_sum(signature, \"Cell adhesion molecules\", library, result=result, compact=False)\nfig.savefig(\"running_sum.png\", bbox_inches='tight')\n\nfig_compact = blitz.plot.running_sum(signature, \"Cell adhesion molecules\", library, result=result, compact=True)\nfig_compact.savefig(\"running_sum_compact.png\", bbox_inches='tight')\n\nfig_table = blitz.plot.top_table(signature, library, result, n=15)\nfig_table.savefig(\"top_table.png\", bbox_inches='tight')\n\n# disable switching of interactive plotting, which can cause issues in jupyter notebooks\n# will plot the figure into the notebook\nfig = blitz.plot.running_sum(signature, \"Cell adhesion molecules\", library, result=result, compact=False, interactive_plot=True)\nfig_table = blitz.plot.top_table(signature, library, result, n=15, interactive_plot=True)\n\n```\n\nThe resulting plots will look like the examples below:\n\n#### running_sum.pdf\n\n<div style=\"bachground-color: white\">\n<img title=\"a title\" alt=\"blitzGSEA sunning_sum\" src=\"https://github.com/MaayanLab/blitzgsea/raw/main/icon/running_sum.png\" width=300>\n</div>\n\n#### running_sum_compact.pdf\n<img title=\"a title\" alt=\"blitzGSEA sunning_sum\" src=\"https://github.com/MaayanLab/blitzgsea/raw/main/icon/running_sum_compact.png\" width=300>\n\n#### top_table.pdf\n<img title=\"a title\" alt=\"blitzGSEA sunning_sum\" src=\"https://github.com/MaayanLab/blitzgsea/raw/main/icon/top_table.png\" width=300>\n\n### Sample shuffling\nThis is the sample shuffling algorithm from GSEApy. It performs a t-test to build signatures for phenotype shuffled groups. The input is a gene expression dataframe, which should be normalized for library size. `groups` is a list containing 0 or 1 describing the corresponding group for the samples in `exprs`. The index of `exprs` are the gene ids matching the gene set library. \n\n```python\nblitz.shuffle.gsea(exprs, library, groups, permutations=50, seed=1)\n```\n\n| parameter name | type | default\t| description |\n|:-----|:---------|:-------------|:------|\n| `exprs`\t| pd.DataFrame | NA\t| Normalized gene expression matrix. |\n| `library` | dictionary | NA | Gene set library. |\n| `groups` | list | NA | Phenotype group labels of samples. Labels are 0 or 1. |\n| `permutations` | int | 1000 | Number of permutations. |\n| `seed`\t| int | 1 | Random state seed. |\n\n# Dependencies\nPython 3.6+\n\n# Attribution\n\nThe statistical tool was developed by the [Ma'ayan Laboratory](https://labs.icahn.mssm.edu/maayanlab/). When using blitzgsea please cite the following reference:\n\nLachmann, Alexander, Zhuorui Xie, and Avi Ma\u2019ayan. \"blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation.\" Bioinformatics (2022).\nhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btac076/6526383?login=false\n\n# References\n\n[1] Lachmann, Alexander, Zhuorui Xie, and Avi Ma\u2019ayan. \"blitzGSEA: efficient computation of gene set enrichment analysis through gamma distribution approximation.\" Bioinformatics (2022).\n\n[2] Subramanian, Aravind, Heidi Kuehn, Joshua Gould, Pablo Tamayo, and Jill P. Mesirov. \"GSEA-P: a desktop application for Gene Set Enrichment Analysis.\" Bioinformatics 23, no. 23 (2007): 3251-3253.\n\n[3] Fang, Zhuoqing, Xinyuan Liu, and Gary Peltz. \"GSEApy: a comprehensive package for performing gene set enrichment analysis in Python.\" Bioinformatics (2022).\n",
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