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topological data analysis of population-scale microbiomes
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README.md

What is tmap?

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.

How to Install?

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 you could also use pip now:

pip install tmap

Now, we also provide a better way to install tmap package. Using docker and dockerfile to build a images to solve all dependency problems.

After clone the repositories, you could use cd tmap; docker build -t tmap .

If you encounter any error like Import error: tkinter, you need to run sudo apt install python-tk or sudo apt install python3-tk.

Documentation

Quick Guides

You can read the Basic Usage of tmap for general use of tmap. Or follow the Microbiome examples for using tmap in microbiome analysis.

For more convenient usage, we implement some executable scripts which will automatically build upon $PATH. For more information about these scripts, you could see. Tutorial of executable scripts

Publication

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

Contact Us

If you have any questions or suggestions, you are welcome to contact us via email: haokui.zhou@gmail.com.

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