GeneCompete is a tool to combine heterogeneous gene expression datasets to order gene importance.
The easy-to-use web-based platform can be accessed through 👉 https://genecompete.streamlit.app/ 👈
Note
We suggest using Python function for large datasets.
We proposed two python function. The output of these functions are pandas dataframe with rating and ranking scores of each gene.
Input | Description |
---|---|
table | Gene expression data: Multiple files where the first column is gene name. These data can be prepared by any tools. |
name | Column name: The interested value that will be used as competing score (in the example is logFC). |
method | Ranking Method: Select 'Win-loss', 'Massey', 'Colley', 'Keener', 'Elo', 'Markov', 'PageRank', or 'BiPagerank' |
reg | Regulation cases: 'Up-regulation' or 'Down-regulation' |
FC | logFC threshold: The large number of genes can consume computational time. Before ranking, datasets are filtered with logFC > FC in case of up-regulation and logFC < -FC for down-regulation. |
- Installation
!git clone https://github.com/panisajan/GeneCompete
- Load data
import pandas as pd
dat1 = pd.read_csv("sample_data/dat1.csv", index_col=0)
dat2 = pd.read_csv("sample_data/dat2.csv", index_col=0)
dat3 = pd.read_csv("sample_data/dat3.csv", index_col=0)
dat4 = pd.read_csv("sample_data/dat4.csv", index_col=0)
dat1.head()
from GeneCompete_Union import*
my_data = [dat1, dat3, dat4]
my_methods = ['Win-loss', 'Massey', 'BiPagerank']
score = GeneCompete_Union(table = my_data, name = 'logFC', method = my_methods, reg = 'Down-regulation', FC = 1)
score.head()
from GeneCompete_Intersect import*
my_data = [dat1, dat2, dat3, dat4]
my_methods = ['Win-loss', 'Keener', 'PageRank']
score = GeneCompete_Intersect(table = my_data, name = 'logFC', method = my_methods, reg = 'Up-regulation', FC = None)
score.head()