/Tissue_Classification

Primary LanguageJupyter Notebook

Tissue_Classification

This is the repository for computational methods associated with the paper "Individual Variability of Protein Expression in Human Tissues" which can be found at https://www.ncbi.nlm.nih.gov/pubmed/30300549.

These are iPython notebooks and associated scripts that were used in a manuscript exploring the classification of human tissues via proteomics data. There are a variety of notebooks which are used to make figures for the manuscript. Each starts from the same basic dataframe. Therefore, the repository should be fairly self-contained, with this df as a starting point. See requirements.txt for required software packages and versions.

How to use

  1. Install python. Python3+ and anaconda recommended. Please refer to https://www.anaconda.com/distribution/.
  2. Create an environment from an environment.yml file. It will create a new environment called tissue_classify.
conda env create -f environment.yml
  1. Activate the new environment.
conda activate tissue_classify
  1. Open jupyter notebooks.
jupyter lab

Notebooks - used to make figures in the manuscript

Please upzip FullPeptideQuant.txt.zip in your local repository to reproduce all the results.

Model_Finalization notebooks

  • Feature selection and classifier parameter tuning
  • Trained_Models directory contains the compressed finalized models, which can be loaded directly into a notebook. This way the finalized models can be used without running the grid searches, which are time-intensive.

Classification.ipynb

  • Code to load and pre-process data
  • Train and test various classifiers. The goal of these classifiers is to see whether we can correctly predict the source tissue of a proteomics sample.

Cohort_Sizes.ipynb

  • Classifying blood plasma and serum with increasingly larger training sets

tSNE_PCA.ipynb

  • Code to produce tSNE and PCA plots for train and test data
  • Includes plots showing diseased samples as open circles, and plots with cell line datasets

Peptide_Boxplots.ipynb

  • Analysis of peptide variability across tissues. Used to create a figure displaying the distinct expression patterns of four archetypal peptides.

Peptide_Thresholding.ipynb

  • Code corresponding to 'Minimal Classifiers'
  • Testing how classifiers perform on test data, with low abundance peptides removed

Auxiliary Scripts

Classification_Utils.py

  • Contains utility functions to perform basic classification processes, including cross-validation and grid searches

build_initial_dataframe.py

  • Script to create dataframes
  • Ensures all dataframes go through the same cleaning and transformation steps:
  • Log2 transform
  • Impute missing values
  • Remove peptides not contained in at least 5 samples of at least 1 tissue
  • Median normalize