For large scale and integrative microbiome research, it is expected to apply advanced data mining techniques in microbiome data analysis.
Topological data analysis (TDA) provides a promising technique for analyzing large scale complex data. The most popular Mapper algorithm is effective in distilling data-shape from high dimensional data, and provides a compressive network representation for pattern discovery and statistical analysis.
tmap is a topological data analysis framework implementing the TDA Mapper algorithm for population-scale microbiome data analysis. We developed tmap to enable easy adoption of TDA in microbiome data analysis pipeline, providing network-based statistical methods for enterotype analysis, driver species identification, and microbiome-wide association analysis of host meta-data.
To install tmap, run:
# (recommend)
git clone https://github.com/GPZ-Bioinfo/tmap.git
cd tmap
python setup.py install
# For some dependency problems. please install following packages.
pip install scikit-bio
R -e "install.packages('vegan',repo='http://cran.rstudio.com/')"
Or using pip:
pip install tmap # it may not the latest version, so be carefull for it.
If you encounter any error like Import error: tkinter, you need to run sudo apt install python-tk or sudo apt install python3-tk.
For more convenient usage, we implement some executable scripts which will automatically build upon $PATH. For more information about these scripts, you could see.
.. toctree:: :maxdepth: 1 basic.rst param.rst vis.rst statistical.rst how2work.rst core_result.rst example.rst scripts.rst api.rst reference.rst FAQ.rst
- You can read the :doc:`Basic Usage of tmap<basic>` for general use of tmap.
- Or follow the :doc:`Microbiome examples<example>` for using tmap in microbiome analysis.
Liao, T., Wei, Y., Luo, M. et al. tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies. Genome Biol 20, 293 (2019) doi:10.1186/s13059-019-1871-4