Welcome to MATTE’s documentation!#

Welcome to raise issues in github https://github.com/gx-Cai/MATTE

Description#

MATTE (Module Alignment of TranscripTomE) is a python package aiming to analysis transcriptome from samples with different phenotypes in a module view. Differiential expression (DE) is commonly used in analysing transcriptome data. But genes are not work alone, they collaborate. Network and module based differential methods are developed in recent years to obtain more information. New problems appears that how to make sure module or network structure is preserved in all of the phenotypes. To that end, we proposed MATTE to find the conserved module and diverged module by treating genes from different phenotypes as individual ones. By doing so, meaningful markers and modules can be found to better understand what’s really difference between phenotypes.

Advantages

In the first place, MATTE merges the data from phenotypes, seeing genes from different phenotypes as new analyzing unite. By doing so, benefits got as follows:

  1. MATTE considering the information in phenotypes in the preprocessing stage, hoping to find more interesting conclusion.

  2. MATTE is actually making transcriptome analysis includes the relationship between phenotypes, which is of significance in cancer or other complex phenotypes.

  3. MATTE can deal with more noise thanks to calculation of relative different expression (RDE) and ignore some of batch effect.

  4. In a module view, “Markers” can be easily transfer to other case but not over fits compare to in a gene view.

  5. The result of MATTE can be easily analysed.

Install#

Install from pip is recommended.

pip install MATTE

All requirements will be installed automatically, but not Intel(R) Extension for Scikit-learn(https://github.com/intel/scikit-learn-intelex). For computers with an Intel CPU or GPU, we highly recommend to install the package for fast clustering. But right now, there is a known issue that it is not compatible with sklearn v1.1, thus if using sklearnex, downgrade sklearn to 1.0.

pip install scikit-learn-intelex
# or install by conda
# conda install scikit-learn-intelex -c conda-forge

Indices and tables#