### Install gseapy with uv

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Install gseapy using the uv package manager. Ensure Rust is installed if pip installation fails.

```shell
# install rust toolchain
curl https://sh.rustup.rs -sSf | sh -s -- -y
export PATH="$PATH:$HOME/.cargo/bin"
# then install via pip or uv
$ pip install gseapy
# or
$ uv add gseapy
```

--------------------------------

### GSEA Command Line Prerank Example

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Example of how to run gseapy prerank analysis using the command line interface.

```bash
# !gseapy prerank -r temp.rnk -g temp.gmt -o prerank_report_temp
```

--------------------------------

### Install gseapy

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Install gseapy using pip or conda.

```bash
pip install gseapy

# if you have conda
conda install -c bioconda gseapy
```

--------------------------------

### Install GSEAPY with Pip

Source: https://github.com/zqfang/gseapy/blob/master/docs/introduction.md

Install the gseapy package using pip. This is an alternative installation method.

```shell
pip install gseapy
```

--------------------------------

### Setup gseapy development environment

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Instructions for cloning the gseapy repository, setting up the development environment using uv, and running tests.

```shell
# clone and set up dev environment (requires Rust toolchain)
$ git clone https://github.com/zqfang/GSEApy.git
$ cd GSEApy
$ uv sync --extra dev

# run tests
$ uv run pytest

# lint and format
```

--------------------------------

### Start Timing

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Records the start time using the time module. This is often used for performance benchmarking of subsequent code blocks.

```python
import time
t1 = time.time()
```

--------------------------------

### Run gseapy commands

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Examples of using gseapy from the command line for various analyses like replot, gsea, prerank, ssgsea, gsva, and enrichr.

```bash
# An example to reproduce figures using replot module.
$ gseapy replot -i ./Gsea.reports -o test
```

```bash
# An example to run GSEA using gseapy gsea module
$ gseapy gsea -d exptable.txt -c test.cls -g gene_sets.gmt -o test
```

```bash
# An example to run Prerank using gseapy prerank module
$ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt -o test
```

```bash
# An example to run ssGSEA using gseapy ssgsea module
$ gseapy ssgsea -d expression.txt -g gene_sets.gmt -o test
```

```bash
# An example to run GSVA using gseapy ssgsea module
$ gseapy gsva -d expression.txt -g gene_sets.gmt -o test
```

```bash
# An example to use enrichr api
# see details for -g input -> ``get_library_name`` 
$ gseapy enrichr -i gene_list.txt -g KEGG_2016 -o test
```

--------------------------------

### Install GSEAPY with Conda

Source: https://github.com/zqfang/gseapy/blob/master/docs/introduction.md

Install the gseapy package using the bioconda channel. This is the recommended method for users with conda.

```shell
conda install -c bioconda gseapy
```

--------------------------------

### Install GSEApy using Conda or Pip

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Install the gseapy package using either the conda/mamba package manager or pip.

```shell
# if you have conda/mamba
$ conda install -c bioconda gseapy
```

```shell
# or pip
$ pip install gseapy
```

```shell
# or uv
```

--------------------------------

### Import Libraries and Setup

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Imports pandas, gseapy, and matplotlib for data manipulation, GSEA analysis, and plotting. Sets up inline plotting and autoreload for interactive environments.

```python
# %matplotlib inline
# %config InlineBackend.figure_format='retina' # mac
%load_ext autoreload
%autoreload 2
import pandas as pd
import gseapy as gp
import matplotlib.pyplot as plt
```

--------------------------------

### Check GSEApy Version

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Displays the installed version of the GSEApy library. Useful for ensuring compatibility and reproducibility.

```python
gp.__version__
```

--------------------------------

### GSEA Command Line Usage

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Example of running GSEA from the command line. Use the '-v' flag for verbose output and '--no-plot' to disable plotting.

```bash
# !gseapy gsea -d ./data/P53_resampling_data.txt \
#              -g KEGG_2016 -c ./data/P53.cls \
#              -o test/gsea_reprot_2 \
#              -v --no-plot \
#              -t phenotype
```

--------------------------------

### Run gseapy functions in Python console (file inputs)

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Examples of running gseapy functions like gsea, prerank, ssgsea, gsva, and replot using file paths as input within a Python console.

```python
import gseapy

# run GSEA.
gseapy.gsea(data='expression.txt', gene_sets='gene_sets.gmt', cls='test.cls', outdir='test')

# run prerank
gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', outdir='test')

# run ssGSEA
gseapy.ssgsea(data="expression.txt", gene_sets= "gene_sets.gmt", outdir='test')

# run GSVA
gseapy.gsva(data="expression.txt", gene_sets= "gene_sets.gmt", outdir='test')

# An example to reproduce figures using replot module.
gseapy.replot(indir='./Gsea.reports', outdir='test')
```

--------------------------------

### Import GSEApy and Scanpy

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Imports the GSEApy library for gene set enrichment analysis and Scanpy for single-cell data processing. Ensure these libraries are installed before running.

```python
import gseapy as gp
import scanpy as sc
```

--------------------------------

### Install GSEAPY with Conda (Bioconda and Conda-Forge)

Source: https://github.com/zqfang/gseapy/blob/master/docs/introduction.md

Install the gseapy package using both conda-forge and bioconda channels. This ensures compatibility with a wider range of dependencies.

```shell
conda install -c conda-forge -c bioconda gseapy
```

--------------------------------

### Command-line Enrichment Analysis

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Example of running gseapy's enrichment analysis directly from the command line using the 'enrichr' command. Includes options for input file, gene sets, verbosity, and output file.

```bash
# !gseapy enrichr -i ./data/gene_list.txt \
#                 -g GO_Biological_Process_2017 \
#                 -v -o test/enrichr_BP
```

--------------------------------

### Run gseapy functions in Python console (DataFrame inputs)

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Examples of using gseapy functions with pandas DataFrames, including gsea, prerank, ssgsea, and gsva, specifying Enrichr library names.

```python
# assign dataframe, and use enrichr library data set 'KEGG_2016'
expression_dataframe = pd.DataFrame()

sample_name = ['A','A','A','B','B','B'] # always only two group,any names you like

# assign gene_sets parameter with enrichr library name or gmt file on your local computer.
gseapy.gsea(data=expression_dataframe, gene_sets='KEGG_2016', cls= sample_names, outdir='test')

# prerank tool
gene_ranked_dataframe = pd.DataFrame()
gseapy.prerank(rnk=gene_ranked_dataframe, gene_sets='KEGG_2016', outdir='test')

# ssGSEA
gseapy.ssgsea(data=expression_dataframe, gene_sets='KEGG_2016', outdir='test')

# gsva
gseapy.gsva(data=expression_dataframe, gene_sets='KEGG_2016', outdir='test')
```

--------------------------------

### Get Yeast Gene Library Names

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Fetches a list of available gene library names for the Yeast organism. The output is a list of strings, and the first 10 are displayed.

```python
yeast = gp.get_library_name(organism='Yeast') 
yeast[:10]
```

--------------------------------

### Get gseapy supported library names

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Retrieves a list of supported gene set library names for use with gseapy functions, such as 'KEGG_2016'.

```python
#see full list of latest enrichr library names, which will pass to -g parameter:
names = gseapy.get_library_name()

# show top 20 entries.
print(names[:20])
```

--------------------------------

### Example Gene List File Content

Source: https://github.com/zqfang/gseapy/blob/master/docs/introduction.md

Content of a sample gene list text file used as input for the gseapy.enrichr() function.

```text
CTLA2B
SCARA3
LOC100044683
CMBL
CLIC6
IL13RA1
TACSTD2
DKKL1
CSF1
CITED1
SYNPO2L
TINAGL1
PTX3
```

--------------------------------

### List Msigdb Categories for a Specific Version

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Lists the available categories within a specific Msigdb database version, for example, for human or mouse data.

```python
# list categories given dbver.  
msig.list_category(dbver="2023.1.Hs") # mouse
```

--------------------------------

### Load IPython Extensions and Libraries

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Loads necessary IPython extensions and core Python libraries for data manipulation and visualization. This is a common setup for interactive data analysis notebooks.

```python
%load_ext autoreload
%autoreload 2
import os
import numpy as np
import pandas as pd 
import matplotlib.pyplot as plt
```

--------------------------------

### Get Available Gene Set Library Names

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Retrieve and print a list of all available gene set library names that can be used for enrichment analysis.

```python
import gseapy
names = gseapy.get_library_name()
print(names)
```

--------------------------------

### GSEApy Enrichr Module Input: Gene List File

Source: https://github.com/zqfang/gseapy/blob/master/docs/introduction.md

Example of providing a gene list from a text file to the gseapy.enrichr() function. Each gene ID should be on a new line.

```python
with open('data/gene_list.txt') as genes:
    print(genes.read())
```

--------------------------------

### Get Supported Enrichr Library Names

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Retrieves a list of all supported Enrichr library names, categorized by species (Human, Mouse, Yeast, Fly, Fish, Worm).

```python
# default: Human
names = gp.get_library_name()
names[:10]
```

--------------------------------

### GSEApy Enrichr Module Input: Python List

Source: https://github.com/zqfang/gseapy/blob/master/docs/introduction.md

Example of providing a gene list as a Python list object to the gseapy.enrichr() function. Gene names should be converted to uppercase.

```python
gene_list = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1',
                'CSF1', 'CITED1', 'SYNPO2L']
```

--------------------------------

### Build Wheel and sdist Locally

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Build the Python wheel and source distribution packages locally.

```bash
uv build
```

--------------------------------

### Get Shape of Ranking Metric

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Retrieves and prints the shape of the ranking metric generated by the GSEA analysis.

```python
res.ranking.shape # raking metric
```

--------------------------------

### Create a CLS file

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Programmatically create a CLS file in Python, defining sample groups for GSEA analysis.

```python
groups = ['C1OE', 'C1OE', 'C1OE', 'Vector', 'Vector', 'Vector']
with open('gsea/edb/C1OE.cls', "w") as cl:
   line = f"{len(groups)} 2 1\n# C10E Vector\n"
   cl.write(line)
   cl.write(" ".join(groups) + "\n")
```

--------------------------------

### Using Yeast Database in gseapy.prerank()

Source: https://github.com/zqfang/gseapy/blob/master/docs/faq.md

For gseapy.prerank(), you can input Enrichr libraries for specific organisms by first retrieving the library names and then optionally a custom GMT dictionary.

```python
# get libraries you'd like to use
gss = gseapy.get_library_name(organism='Yeast')
# get a custom gmt_dict
gmt_dict = gseapy.get_library('GO_Biological_Process_2018', organism='Yeast')
# run
prn_res = gseapy.prerank( ..., gene_sets=gmt_dict, ...)
```

--------------------------------

### Get Ranking Data Shape

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Retrieves the dimensions (number of rows and columns) of the loaded ranking data DataFrame.

```python
rnk.shape
```

--------------------------------

### Get Shape of Down-regulated Genes

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Returns the shape (number of rows and columns) of the DataFrame containing significantly down-regulated genes.

```python
degs_dw.shape
```

--------------------------------

### Command Line Usage of ssGSEA

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Demonstrates how to run ssGSEA analysis directly from the command line using the gseapy tool. This is useful for batch processing or integrating into shell scripts.

```bash
# !gseapy ssgsea -d ./data/testSet_rand1200.gct \
#                -g data/temp.gmt \
#                -o test/ssgsea_report2  \
#                -p 4 --no-plot 
```

--------------------------------

### Get Shape of Up-regulated Genes

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Returns the shape (number of rows and columns) of the DataFrame containing significantly up-regulated genes.

```python
degs_up.shape
```

--------------------------------

### Reproduce GSEA Desktop Plots via Command Line

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Execute the replot command from the command line to reproduce GSEA desktop plots. Specify the input and output directories.

```bash
gseapy replot -i gsea -o gseapy_out
```

--------------------------------

### Format Code with Ruff

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Use this command to format the gseapy codebase using Ruff.

```bash
uv run ruff format gseapy
```

--------------------------------

### Run GSEA with Manual Phenotype Setting

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Initialize GSEA with a gene expression dataset, a specific gene set (e.g., KEGG_2016), and a class vector. Manually define positive and negative phenotypes (e.g., 'AML' and 'ALL') before running the analysis. This is useful when class labels are not directly inferable from the cls file.

```python
from gseapy import GSEA
gs = GSEA(data=gene_exp,
gene_sets='KEGG_2016', 
classes = class_vector, # cls=class_vector
 # set permutation_type to phenotype if samples >=15
permutation_type='phenotype', 
permutation_num=1000, # reduce number to speed up test
outdir=None, 
method='signal_to_noise',
threads=4, seed= 8)
gs.pheno_pos = "AML"
gs.pheno_neg = "ALL"
gs.run()
```

--------------------------------

### List Available Msigdb Versions

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Lists all available database versions for querying the Msigdb API.

```python
# list msigdb version you wanna query
msig.list_dbver()
```

--------------------------------

### Initialize Msigdb API

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Initializes the Msigdb API client for accessing the Molecular Signatures Database.

```python
from gseapy import Msigdb
```

```python
msig = Msigdb()
```

--------------------------------

### Initialize SciPalette Colormap

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Initializes the SciPalette object and creates a colormap. This is likely used for subsequent visualizations, although the colormap itself is not directly displayed in this snippet.

```python
from gseapy.scipalette import SciPalette
sci = SciPalette()
NbDr = sci.create_colormap()
# NbDr
```

--------------------------------

### Reproduce GSEA Desktop Plots with replot()

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Use the replot function to reproduce GSEA desktop plots. This requires an input directory containing specific GSEA output files (edb folder with .cls, .gmt, .rnk, and results.edb).

```python
import gseapy
gseapy.replot(indir ='gsea', outdir = 'gseapy_out')
```

--------------------------------

### Command line GSVA usage

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Demonstrates the command-line interface for running GSVA. It specifies the expression data file, gene set file, and the output directory for the report.

```bash
# !gseapy ssgsea -d ./tests/data/expr.gsva.csv \
#                -g ./tests/data/geneset.gsva.gmt \
#                -o test/gsva_report
```

--------------------------------

### Test Rust Extension

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Run tests specifically for the Rust extension module.

```bash
cargo test --features=extension-module
```

--------------------------------

### Importing gseapy

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Imports the gseapy library, typically done at the beginning of a Python script or notebook to access its functionalities.

```python
import gseapy as gp
```

--------------------------------

### Initialize Network Visualization

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Imports the `enrichment_map` function for building network visualizations from GSEA results. The generated nodes and edges can be used for external visualization tools like Cytoscape.

```python
from gseapy import enrichment_map
```

--------------------------------

### Replot module initialization

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Initializes the replot module within a Python console to generate plots from GSEA results. It requires specifying the input directory containing the GSEA output and the desired output directory for the plots.

```python
# run command inside python console
rep = gp.replot(indir="./tests/data", outdir="tests/replot_test")
```

--------------------------------

### Check Code with Ruff

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Use this command to check the gseapy codebase for linting errors with Ruff.

```bash
uv run ruff check gseapy
```

--------------------------------

### Initialize Biomart API

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Initializes the Biomart API client. This API is useful for retrieving gene-related data from biological databases.

```python
from gseapy import Biomart 
bm = Biomart()

```

--------------------------------

### Importing NetworkX Library

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Imports the `networkx` library, commonly used for graph and network analysis in Python. This import is often a prerequisite for advanced data visualization or manipulation tasks.

```python
import networkx as nx
```

--------------------------------

### Running ssGSEA with Permutations

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Performs ssGSEA analysis with permutations enabled. Setting `permutation_num > 0` makes ssGSEA behave like the prerank tool. It's recommended to use this only if you understand its implications.

```python
ss_permut = gp.ssgsea(data="./tests/extdata/Leukemia_hgu95av2.trim.txt",
               gene_sets="./tests/extdata/h.all.v7.0.symbols.gmt", 
               outdir=None, 
               sample_norm_method='rank', # choose 'custom' for your custom metric
               permutation_num=20, # set permutation_num > 0, it will act like prerank tool
               no_plot=True, # skip plotting, because you don't need these figures
               processes=4, seed=9)
ssp_permut.res2d.head(5)
```

--------------------------------

### Run gseapy.enrichr with list or file

Source: https://github.com/zqfang/gseapy/blob/master/README.rst

Demonstrates how to use the gseapy.enrichr function with a Python list of genes or a text file containing gene names.

```python
# assign a list object to enrichr
gl = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1', 'CSF1',
     'SYNPO2L', 'TINAGL1', 'PTX3', 'BGN', 'HERC1', 'EFNA1', 'CIB2', 'PMP22', 'TMEM173']

gseapy.enrichr(gene_list=gl, gene_sets='KEGG_2016', outdir='test')

# or a txt file path.
gseapy.enrichr(gene_list='gene_list.txt', gene_sets='KEGG_2016',
               outdir='test', cutoff=0.05, format='png' )
```

--------------------------------

### Using Yeast Database in gseapy.enrichr()

Source: https://github.com/zqfang/gseapy/blob/master/docs/faq.md

To use the Yeast database with gseapy.enrichr(), specify 'organism="Yeast"'. This is necessary when library names might be ambiguous across different organisms.

```python
gss = gseapy.get_library_name(organism='Yeast')
enr = gseapy.enrichr(gene_list=...,
                    gene_sets=gss,
                    organism='Yeast',
                    )
```

--------------------------------

### Single Sample GSEA with File Input

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Performs ssGSEA using a text file for data and a GMT file for gene sets. 'sample_norm_method' can be set to 'rank' or 'custom'.

```python
import gseapy as gp
# txt, gct file input
ss = gp.ssgsea(data='./tests/extdata/Leukemia_hgu95av2.trim.txt',
               gene_sets='./tests/extdata/h.all.v7.0.symbols.gmt',
               outdir=None, 
               sample_norm_method='rank', # choose 'custom' will only use the raw value of `data`
               no_plot=True)
```

--------------------------------

### Download Mouse Hallmark Gene Sets from Msigdb

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Downloads mouse hallmark gene sets from the Msigdb database using a specified version. The GMT file can be downloaded from the provided URL.

```python
# mouse hallmark gene sets
gmt = msig.get_gmt(category='mh.all', dbver="2023.1.Mm")
```

--------------------------------

### Run GSEA Analysis

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Placeholder for running the GSEA analysis using the parsed data and specified parameters.

```python
# run gsea

```

--------------------------------

### Read and print CLS file content

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Read the content of a CLS file and print it to the console. This file specifies sample attributes for GSEA.

```python
with open('gsea/edb/C1OE.cls') as cls:
    print(cls.read())

# or assign a list object to parameter 'cls' like this
# cls=['C1OE', 'C1OE', 'C1OE', 'Vector', 'Vector', 'Vector']
```

--------------------------------

### Command line replot usage

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Shows the command-line usage for the replot function in gseapy. This command is used to regenerate plots from existing GSEA analysis results, specifying the input directory and output directory.

```bash
# !gseapy replot -i data -o test/replot_test
```

--------------------------------

### Convert Seurat Data to Scanpy Format (R)

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

This R code snippet demonstrates how to load Seurat data, save it as an H5Seurat file, and then convert it to the AnnData (.h5ad) format compatible with Scanpy. Requires Seurat and SeuratDisk packages.

```r
library(Seurat)
library(SeuratDisk)
ifnb = SeuratData::LoadData("ifnb")
SaveH5Seurat(ifnb, "ifnb.h5seurat", overwrite = T)
Convert("ifnb.h5seurat", "ifnb.h5ad", overwrite = T)
```

--------------------------------

### Load Data from Tab-Separated File

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Reads a tab-separated ranking file into a pandas DataFrame, setting the first column as the index.

```python
ssdf = pd.read_csv("./tests/data/temp.rnk", header=None,index_col=0,  sep="\t")
ssdf.head()
```

--------------------------------

### Using Yeast Database with gseapy.enrichr

Source: https://github.com/zqfang/gseapy/wiki/FAQ

When using Enrichr databases other than Human, specify the 'organism' argument to avoid library name conflicts. First, retrieve the library names for the desired organism.

```python
gss = gseapy.get_library_name('Yeast')
enr = gseapy.enrichr(gene_list=...,
                     gene_sets=gss, 
                     organism='Yeast',# don't forget to set organism="Yeast"
                     ...)
```

--------------------------------

### Visualize Network Graph with Node Attributes

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Draws a NetworkX graph using a spiral layout, coloring nodes by NES and sizing them by Hits_ratio. Edges are weighted by jaccard_coef.

```python
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 8))

# init node cooridnates
pos=nx.layout.spiral_layout(G)
#node_size = nx.get_node_attributes()
# draw node
nx.draw_networkx_nodes(G, 
                       pos=pos, 
                       cmap=plt.cm.RdYlBu, 
                       node_color=list(nodes.NES),
                       node_size=list(nodes.Hits_ratio *1000))
# draw node label
nx.draw_networkx_labels(G, 
                        pos=pos, 
                        labels=nodes.Term.to_dict())
# draw edge
edge_weight = nx.get_edge_attributes(G, 'jaccard_coef').values()
nx.draw_networkx_edges(G, 
                       pos=pos, 
                       width=list(map(lambda x: x*10, edge_weight)), 
                       edge_color='#CDDBD4')
plt.show()
```

--------------------------------

### List of Available Gene Set Libraries

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

A comprehensive list of gene set libraries available for use with gseapy.

```json
['Genome_Browser_PWMs',
'TRANSFAC_and_JASPAR_PWMs',
'ChEA_2013',
'Drug_Perturbations_from_GEO_2014',
'ENCODE_TF_ChIP-seq_2014',
'BioCarta_2013',
'Reactome_2013',
'WikiPathways_2013',
'Disease_Signatures_from_GEO_up_2014',
'KEGG_2013',
'TF-LOF_Expression_from_GEO',
'TargetScan_microRNA',
'PPI_Hub_Proteins',
'GO_Molecular_Function_2015',
'GeneSigDB',
'Chromosome_Location',
'Human_Gene_Atlas',
'Mouse_Gene_Atlas',
'GO_Cellular_Component_2015',
'GO_Biological_Process_2015',
'Human_Phenotype_Ontology',
'Epigenomics_Roadmap_HM_ChIP-seq',
'KEA_2013',
'NURSA_Human_Endogenous_Complexome',
'CORUM',
'SILAC_Phosphoproteomics',
'MGI_Mammalian_Phenotype_Level_3',
'MGI_Mammalian_Phenotype_Level_4',
'Old_CMAP_up',
'Old_CMAP_down',
'OMIM_Disease',
'OMIM_Expanded',
'VirusMINT',
'MSigDB_Computational',
'MSigDB_Oncogenic_Signatures',
'Disease_Signatures_from_GEO_down_2014',
'Virus_Perturbations_from_GEO_up',
'Virus_Perturbations_from_GEO_down',
'Cancer_Cell_Line_Encyclopedia',
'NCI-60_Cancer_Cell_Lines',
'Tissue_Protein_Expression_from_ProteomicsDB',
'Tissue_Protein_Expression_from_Human_Proteome_Map',
'HMDB_Metabolites',
'Pfam_InterPro_Domains',
'GO_Biological_Process_2013',
'GO_Cellular_Component_2013',
'GO_Molecular_Function_2013',
'Allen_Brain_Atlas_up',
'ENCODE_TF_ChIP-seq_2015',
'ENCODE_Histone_Modifications_2015',
'Phosphatase_Substrates_from_DEPOD',
'Allen_Brain_Atlas_down',
'ENCODE_Histone_Modifications_2013',
'Achilles_fitness_increase',
'Achilles_fitness_decrease',
'MGI_Mammalian_Phenotype_2013',
'BioCarta_2015',
'HumanCyc_2015',
'KEGG_2015',
'NCI-Nature_2015',
'Panther_2015',
'WikiPathways_2015',
'Reactome_2015',
'ESCAPE',
'HomoloGene',
'Disease_Perturbations_from_GEO_down',
'Disease_Perturbations_from_GEO_up',
'Drug_Perturbations_from_GEO_down',
'Genes_Associated_with_NIH_Grants',
'Drug_Perturbations_from_GEO_up',
'KEA_2015',
'Single_Gene_Perturbations_from_GEO_up',
'Single_Gene_Perturbations_from_GEO_down',
'ChEA_2015',
'dbGaP',
'LINCS_L1000_Chem_Pert_up',
'LINCS_L1000_Chem_Pert_down',
'GTEx_Tissue_Sample_Gene_Expression_Profiles_down',
'GTEx_Tissue_Sample_Gene_Expression_Profiles_up',
'Ligand_Perturbations_from_GEO_down',
'Aging_Perturbations_from_GEO_down',
'Aging_Perturbations_from_GEO_up',
'Ligand_Perturbations_from_GEO_up',
'MCF7_Perturbations_from_GEO_down',
'MCF7_Perturbations_from_GEO_up',
'Microbe_Perturbations_from_GEO_down',
'Microbe_Perturbations_from_GEO_up',
'LINCS_L1000_Ligand_Perturbations_down',
'LINCS_L1000_Ligand_Perturbations_up',
'LINCS_L1000_Kinase_Perturbations_down',
'LINCS_L1000_Kinase_Perturbations_up',
'Reactome_2016',
'KEGG_2016',
'WikiPathways_2016',
'ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X',
'Kinase_Perturbations_from_GEO_down',
'Kinase_Perturbations_from_GEO_up',
'BioCarta_2016',
'Humancyc_2016',
'NCI-Nature_2016',
'Panther_2016']
```

--------------------------------

### Parse GSEA Class File

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Parses a .cls file to extract phenotype labels and class vectors for GSEA analysis.

```python
import gseapy as gp
import pandas as pd

phenoA, phenoB, class_vector =  gp.parser.gsea_cls_parser("./tests/extdata/Leukemia.cls")
```

--------------------------------

### Run GSEA with Phenotype Permutation

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Performs Gene Set Enrichment Analysis using the GSEA algorithm. This snippet uses phenotype permutation and specifies the number of permutations, the GSEA method, and output directory.

```python
res = gp.gsea(data=bdata.to_df().T, # row -> genes, column-> samples
        gene_sets="GO_Biological_Process_2021",
        cls=bdata.obs.stim,
        permutation_num=1000,
        permutation_type='phenotype', 
        outdir=None,
        method='s2n', # signal_to_noise
        threads= 16)
t2=time.time()
print(t2-t1)
```

--------------------------------

### Import Plotting Functions

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Imports the necessary functions for creating bar plots and dot plots from the gseapy library.

```python
# simple plotting function
from gseapy import barplot, dotplot
```

--------------------------------

### Run GSEA with Enrichr Library

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Perform Gene Set Enrichment Analysis using a specified gene expression dataset, gene sets from an Enrichr library, and a class vector. Ensure the permutation type is set to 'phenotype' if sample size is 15 or more.

```python
gs_res = gp.gsea(data=gene_exp, # or data='./P53_resampling_data.txt'
gene_sets='./tests/extdata/h.all.v7.0.symbols.gmt', # or enrichr library names
 cls= "./tests/extdata/Leukemia.cls", # cls=class_vector
 # set permutation_type to phenotype if samples >=15
permutation_type='phenotype', 
permutation_num=1000, # reduce number to speed up test
outdir=None,  # do not write output to disk
method='signal_to_noise',
threads=4, seed= 7)
```

--------------------------------

### Convert Human Gene Symbols to Mouse Symbols for GMT file

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Creates a dictionary mapping human gene symbols to their corresponding mouse symbols. This is useful for running GSEA on non-human species using human-formatted GMT files.

```python
# get a dict symbol mappings
h2m_dict = {}
for i, row in h2m.loc[:,["external_gene_name", "mmusculus_homolog_associated_gene_name"]].iterrows():
    if row.isna().any(): continue
    h2m_dict[row['external_gene_name']] = row["mmusculus_homolog_associated_gene_name"]
# read gmt file into dict
kegg = gp.read_gmt(path="tests/extdata/enrichr.KEGG_2016.gmt")
print(kegg['MAPK signaling pathway Homo sapiens hsa04010'][:10])
```

```python
kegg_mouse = {}
for term, genes in kegg.items():
    new_genes = []
    for gene in genes:
        if gene in h2m_dict:
            new_genes.append(h2m_dict[gene])
    kegg_mouse[term] = new_genes
print(kegg_mouse['MAPK signaling pathway Homo sapiens hsa04010'][:10])
```

--------------------------------

### Parse Enrichr Library into Dictionary

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Downloads a specified Enrichr library (e.g., GO Molecular Function) for a given organism and prints a subset of its parsed dictionary representation. This allows direct access to gene sets within the library.

```python
## download library or read a .gmt file
go_mf = gp.get_library(name='GO_Molecular_Function_2018', organism='Yeast')
print(go_mf['ATP binding (GO:0005524)'])
```

--------------------------------

### Display First Few Rows of Observation Metadata

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Shows the first five rows of the observation (cell) metadata DataFrame. Useful for inspecting cell annotations, counts, and experimental conditions.

```python
adata.obs.head()
```

--------------------------------

### Load Ranking Data

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Loads a ranking file (e.g., .rnk) into a pandas DataFrame for use with the prerank module. The file is expected to be tab-separated with gene names as the index.

```python
rnk = pd.read_csv("./tests/data/temp.rnk", header=None, index_col=0, sep="\t")
rnk.head()
```

--------------------------------

### Display Class Vector

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Prints the class vector obtained from parsing a .cls file, indicating group attributes for each sample.

```python
#class_vector used to indicate group attributes for each sample
print(class_vector)
```

--------------------------------

### GSVA with TXT and GMT files

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Performs Gene Set Variation Analysis (GSVA) using a text file for expression data and a GMT file for gene sets. The output directory is set to None, meaning results are not saved to disk by default.

```python
import gseapy as gp
es = gp.gsva(data='./tests/extdata/Leukemia_hgu95av2.trim.txt',
             gene_sets='./tests/extdata/h.all.v7.0.symbols.gmt',
             outdir=None)
```

--------------------------------

### Read and print GMT file content

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Read the content of a GMT file, which defines gene sets for enrichment analysis, and print it.

```python
with open('gsea/edb/gene_sets.gmt') as gmt:
    print(gmt.read())
```

--------------------------------

### Build NetworkX Graph from Pandas Edges

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Constructs a NetworkX graph object from the edges DataFrame. It also ensures that all nodes from the nodes DataFrame are included in the graph, even if they have no edges.

```python
G = nx.from_pandas_edgelist(edges, 
                            source='src_idx', 
                            target='targ_idx', 
                            edge_attr=['jaccard_coef', 'overlap_coef', 'overlap_genes'])

# Add missing node if there is any
for node in nodes.index:
    if node not in G.nodes():
        G.add_node(node)
```

--------------------------------

### Print a Specific Gene Set from Msigdb

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Prints the list of genes belonging to the 'HALLMARK_WNT_BETA_CATENIN_SIGNALING' gene set from the downloaded Msigdb GMT data.

```python
print(gmt['HALLMARK_WNT_BETA_CATENIN_SIGNALING'])
```

--------------------------------

### Display Preranked Enrichment Results Head

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Displays the first five rows of the results from the preranked gene set enrichment analysis. This helps in understanding the top enriched pathways or gene sets.

```python
pre_res.res2d.head(5)
```

--------------------------------

### Print Phenotype Labels

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Displays the phenotype labels for positively correlated samples (phenoA) and negatively correlated samples (phenoB).

```python
print("positively correlated: ", phenoA)
print("negtively correlated: ", phenoB)
```

--------------------------------

### Offline Enrichment Analysis with Local Gene Sets

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Performs an offline enrichment analysis using a local gene list file and a GMT file for gene sets. It also shows how to handle a dictionary of gene sets and skips unknown libraries.

```python
# NOTE: `enrich` instead of `enrichr`
enr2 = gp.enrich(gene_list="./tests/data/gene_list.txt", # or gene_list=glist
                 gene_sets=["./tests/data/genes.gmt", "unknown", kegg ], # kegg is a dict object
                 background=None, # or "hsapiens_gene_ensembl", or int, or text file, or a list of genes
                 outdir=None, 
                 verbose=True)

```

--------------------------------

### Generate Gene Expression Heatmap

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Creates a heatmap visualization for a specific gene set identified by GSEA. It extracts the lead genes for a given term and plots their expression levels.

```python
i = 7
genes = res.res2d.Lead_genes.iloc[i].split(";")
ax = gp.heatmap(df = res.heatmat.loc[genes], 
           z_score=None, 
           title=res.res2d.Term.iloc[i], 
           figsize=(6,5), 
           cmap=plt.cm.viridis, 
           xticklabels=False)
```

--------------------------------

### Prerank Gene Set Enrichment Analysis

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Performs preranked gene set enrichment analysis using specified parameters. Ensure the input file and gene sets are correctly provided. The `threads` parameter can accelerate computation.

```python
import gseapy as gp

pre_res = gp.prerank(rnk="./tests/data/temp.rnk", # or rnk = rnk,
gene_sets='KEGG_2016',
threads=4,
min_size=5,
max_size=1000,
permutation_num=1000, # reduce number to speed up testing
outdir=None, # don't write to disk 
seed=6,
verbose=True, # see what's going on behind the scenes
)
```

--------------------------------

### Display Enrichr Results Head

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Displays the first few rows of the enrichment analysis results from Enrichr.

```python
enr_bg.results.head()
```

--------------------------------

### Map Mouse Genes to Human Homologs using Biomart

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Uses the Biomart API to query for mouse genes and their human homologs, retrieving Ensembl IDs and associated gene names.

```python
from gseapy import Biomart 
bm = Biomart()
# note the dataset and attribute names are different
m2h = bm.query(dataset='mmusculus_gene_ensembl',
               attributes=['ensembl_gene_id','external_gene_name',
                           'hsapiens_homolog_ensembl_gene',
                           'hsapiens_homolog_associated_gene_name'])

h2m = bm.query(dataset='hsapiens_gene_ensembl',
               attributes=['ensembl_gene_id','external_gene_name',
                           'mmusculus_homolog_ensembl_gene',
                           'mmusculus_homolog_associated_gene_name'])
```

--------------------------------

### Display GSEA Results Table

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Displays the first 10 rows of the GSEA results table, which includes terms, enrichment scores, p-values, and associated genes.

```python
res.res2d.head(10)
```

--------------------------------

### Display Offline Enrichment Results Head

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Displays the first few rows of the offline enrichment analysis results.

```python
enr2.results.head()
```

--------------------------------

### Enrichr with Background Gene List

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Performs an Enrichr analysis using a provided gene list and a background file. The organism argument is ignored when a background is specified.

```python
enr_bg = gp.enrichr(gene_list=gene_list,
                 gene_sets=['MSigDB_Hallmark_2020','KEGG_2021_Human'], 
                 background="tests/data/background.txt",
                 outdir=None) # don't write to disk 

```

--------------------------------

### Displaying Multi-Dataset Dot Plot

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Generates a dot plot to visualize multi-dataset enrichment results. Requires `gseapy` and `matplotlib`. Customize plot appearance with arguments like `figsize`, `x`, `x_order`, `title`, `cmap`, `size`, and `show_ring`.

```python
ax = gp.dotplot(enr_res,figsize=(3,5), 
                x='UP_DW', 
                x_order = ["UP","DOWN"], 
                title="GO_BP", 
                cmap = NbDr.reversed(), 
                size=3, 
                show_ring=True)
ax.set_xlabel("")
plt.show()
```

--------------------------------

### Dot Plot Visualization

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Generates a dot plot for gene set enrichment results. Ensure 'ofname' is set to save the figure.

```python
ax = dotplot(gs_res.res2d, 
             column="FDR q-val",
             title='KEGG_2021_Human',
             cmap=plt.cm.viridis, 
             size=5, 
             figsize=(4,5), cutoff=1)
```

--------------------------------

### Enrichr with a List of Genes

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Perform enrichment analysis by providing a Python list of gene IDs. Specify the gene set library and an output file name.

```python
l = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1', 'CSF1',
     'SYNPO2L', 'TINAGL1', 'PTX3', 'BGN', 'HERC1', 'EFNA1', 'CIB2', 'PMP22', 'TMEM173']

gseapy.enrichr(gene_list=l, gene_sets='KEGG_2016', outfile='test')
```

--------------------------------

### Display Head of Edges DataFrame

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Displays the first few rows of the edges DataFrame, detailing the connections between nodes, including source and target indices, names, and overlap coefficients.

```python
edges.head()
```

--------------------------------

### Load and view gene expression data

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Load gene expression data from a text file into a pandas DataFrame and display the first few rows. This format is compatible with GSEA desktop.

```python
import pandas as pd
df = pd.read_table('./test/gsea_data.txt')
df.head()

#or assign dataframe to the parameter 'data'
```

--------------------------------

### Enrichr with a Gene List File

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_tutorial.md

Perform enrichment analysis using a text file containing gene IDs, one per line. Specify the gene set library and an output file name.

```python
gseapy.enrichr(gene_list='gene_list.txt',  gene_sets='KEGG_2016', outfile='test')
```

--------------------------------

### Perform Enrichr Over-representation Analysis

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Conducts an over-representation analysis using Enrichr web services with a provided gene list and specified gene sets. The organism is set to 'human'. The output is returned as an object, and the top 5 results are displayed.

```python
# if you are only intrested in dataframe that enrichr returned, please set outdir=None
enr = gp.enrichr(gene_list=gene_list, # or "./tests/data/gene_list.txt", 
                 gene_sets=['MSigDB_Hallmark_2020','KEGG_2021_Human'], 
                 organism='human', # don't forget to set organism to the one you desired! e.g. Yeast
                 outdir=None, # don't write to disk 
                )

```

```python
# obj.results stores all results
enr.results.head(5)
```

--------------------------------

### Plot Multiple Pathway Enrichments

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Generates plots for multiple enriched pathways. The `terms` parameter accepts a list of terms. `show_ranking=True` displays the gene ranking on a secondary y-axis. Adjust `figsize` for plot dimensions.

```python
axs = pre_res.plot(terms=terms[1:5], 
                   #legend_kws={'loc': (1.2, 0)}, # set the legend loc
                   show_ranking=True, # whether to show the second yaxis
                   figsize=(3,4)
                  )
```

--------------------------------

### Count Cells per Annotation and Condition

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Groups cells by their 'seurat_annotations' and 'stim' (condition) and counts the number of cells in each group. Helps understand the distribution of cell types across conditions.

```python
adata.obs.groupby('seurat_annotations')['stim'].value_counts()
```

--------------------------------

### Generate Dot Plot for GSEA Results

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Creates a dot plot to visualize GSEA results, showing enrichment significance based on a specified column (e.g., 'FDR q-val'). Customize dot size, colormap, and cutoff. `show_ring=False` removes the ring effect.

```python
from gseapy import dotplot
import matplotlib.pyplot as plt

ax = dotplot(pre_res.res2d, 
             column="FDR q-val",
             title='KEGG_2016',
             cmap=plt.cm.viridis, 
             size=6, # adjust dot size
             figsize=(4,5), cutoff=0.25, show_ring=False)

```

--------------------------------

### Display Differential Gene Expression Head

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Displays the first few rows of the differential gene expression DataFrame. This is useful for quickly inspecting the structure and content of the results.

```python
degs.head()
```

--------------------------------

### Plot Preranked Enrichment Results

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Generates a plot for the top 5 terms from the preranked enrichment analysis results. This visualization aids in interpreting the enrichment findings.

```python
term2 = pre_res.res2d.Term
axes = pre_res.plot(terms=term2[:5])
```

--------------------------------

### Generate Nodes and Edges for Enrichment Map

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Converts enrichment results into nodes and edges suitable for network visualization. This is a prerequisite for building the enrichment map graph.

```python
nodes, edges = gp.enrichment_map(res.res2d)
```

--------------------------------

### Custom Gene Sets Dictionary Input

Source: https://github.com/zqfang/gseapy/blob/master/docs/faq.md

When using custom GMT files, the 'gene_sets' argument can accept a dictionary. This is useful for defining your own gene sets for analysis.

```python
gene_sets = {
          "term_1": ["gene_A", "gene_B", ...],
          "term_2": ["gene_B", "gene_C", ...],
           ...
          "term_100": ["gene_A", "gene_T", ...]
         }
```

--------------------------------

### Read H5AD Data with Scanpy

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Loads single-cell data from an H5AD file into a Scanpy AnnData object. This file is typically generated from Seurat or other single-cell analysis tools.

```python
adata = sc.read_h5ad("tests/data/ifnb.h5ad") # data from SeuratData::ifnb
```

--------------------------------

### Generating Bar Plot for Enrichment Results

Source: https://github.com/zqfang/gseapy/blob/master/docs/singlecell_example.ipynb

Creates a bar plot to visualize enrichment results, useful for comparing enrichment scores across groups. Requires `gseapy`. Customize with `figsize`, `group`, `title`, and `color`.

```python
ax = gp.barplot(enr_res, figsize=(3,5), 
                group ='UP_DW', 
                title ="GO_BP", 
                color = ['b','r'])

```

--------------------------------

### Displaying Top Results from ssGSEA

Source: https://github.com/zqfang/gseapy/blob/master/docs/gseapy_example.ipynb

Shows the top results of an ssGSEA analysis, sorted by 'Name'. This is useful for quickly inspecting the most significant gene sets.

```python
ss.res2d.sort_values('Name').head()
```