metabotk 0.1.36

Python toolkit for working with metabolomics data

MetabolomicsToolKit

MetabolomicsToolKit (metabotk) is a Python library for working with metabolomics data. It provides a variety of tools and methods for importing, exploring, manipulating and analyzing metabolomics data.

The library is designed to be easy to use and provides a simple interface for working with metabolomics data. It is also highly extensible and allows users to easily add new methods and tools.

Features

• Import and export metabolomics data from various sources (e.g. Metabolon) and formats (e.g. excel files, tables)
• Explore and manipulate metabolomics data
• Impute missing values
• Normalize data
• Perform feature selection and dimensionality reduction
• Plot data (e.g. metabolite abundance, PCA)

How to install

Install with pip:

pip install metabotk

Dependencies

• pandas
• numpy
• math
• scikit-learn
• seaborn
• boruta_py for feature selection using Boruta
• skloess for normalization with LOESS
• pyserrf for normalization with SERRF

How to use

!!! Documentation is still a work in progress !!!

In-depth documentation about each module can be found here:
https://metabolomics-toolkit.readthedocs.io/en/latest/

Import the library and initiate the class

from metabotk import MetaboTK

dataset = MetaboTK(data_provider="metabolon", sample_id_column='Sample', metabolite_id_column="CHEM_ID",)

Import the data in tabular or excel format

#TABLES
dataset.import_tables(data="data.tsv", sample_metadata="samples.tsv", chemical_annotation="config/metabolites.tsv")

#EXCEL -> the sheet names for data, sample metadata and chemical annotation must be specified
dataset.import_excel(file_path="dataset.xlsx", sample_metadata = "sample_metadata", chemical_annotation = "chemical_annotation", data_sheet = "peak_data",

Get some statistics about the dataset

#SAMPLE LEVEL STATS
sample_stats = dataset.stats.sample_stats()

#METABOLITE LEVEL STATS
metabolite_stats = dataset.stats.metabolite_stats()

Get a PCA from the data and plot it

pca=dataset.dimensionality_reduction.get_pca(n_components=3)

datset.visualization.plot_pca(pca=pca, hue='treatment')