Maschine learning methods for predicting cell-cycle phase from scRNA-Seq data

By Ron Fechtner

Ludwig-Maximilians-Universität München / Technische Universität München

This is the supplementary for the Bachelor's Thesis on Maschine learning methods for predicting cell-cycle phase from scRNA-Seq data.

Here you will find the a digital copy of the thesis, the codes, data, images, restul tables and juypter notebooks. Most of the chapters have a associated jupyter notebook file within the notebook folder that shows how the results were generated. This Readme is an example of such an notebook. All code can be directly executed or modified. For further information please see: http://jupyter.readthedocs.io/en/latest/index.html

If you don't want to install juypter notebook on your local machine you can visit http://nbviewer.jupyter.org/github/rfechtner/pypairs_supplementary/tree/master/notebooks/ for a online view (read only).

The general structure of this folder is:

import os

for root, dirs, files in os.walk("./"):
    level = root.replace("./", '').count(os.sep)
    indent = ' ' * 4 * (level)
    print()
    folder = os.path.basename(root)
    if not folder.startswith("."):
        print('{}{}/'.format(indent, folder))
        subindent = ' ' * 4 * (level + 1)
        for f in files:
            print('{}{}'.format(subindent, f))

/
    Readme.ipynb
    Readme.md


code/
    helper.py
    pypairs.py
    __init__.py

    __pycache__/
        helper.cpython-36.pyc
        pypairs.cpython-36.pyc

data/
    biomart_human-genes.txt
    biomart_mouse-human-orthologs.txt
    cell_cycle_genes.csv
    cyclebase_top1000_genes.tsv
    E-MTAB-3929.processed.1_counts.txt
    E-MTAB-3929_annotation.txt
    E-MTAB-6142_human.csv
    go_0007049_homoSapiens.csv
    GSE53481_humanRNAseq.txt
    GSE64016_H1andFUCCI_normalized_EC_human.csv
    GSE71456_Samples_RPKM.csv
    GSE75748_bulk_cell_type_ec.csv
    GSE75748_bulk_time_course_ec.csv
    GSE75748_sc_cell_type_ec.csv
    GSE75748_sc_time_course_ec.csv
    mESC_dataset_mouse.txt
    mouse_pretrained-pairs.json
    Non_norm.PolyA_NamedByAlex_human.csv
    regev_lab_cell_cycle_genes.txt

images/

    application/
        cell_lineage_e5-e7.pdf
        ebv_circle.pdf
        ebv_circle.png
        ebv_line.pdf
        ebv_line.png
        ebv_scatter.pdf
        prediction_e3-e7.pdf
        prediction_e3-e7.png
        prediction_e3-ee5.pdf
        prediction_e3-ee5.png
        prediction_e5-e7.pdf
        prediction_e5-e7.png

    differences/
        sandbag-speed.pdf
        sandbag-speed.png

    evaluation/
        E-MTAB-3929.sdrf.txt
        hESC-scatter.pdf
        hESC-scatter.png
        hESC-scatter.svg
        hPSC-assign-all-scatter.pdf
        hPSC-assign-all.pdf
        hPSC-assign-all.png
        hPSC-assign-h1-scatter.pdf
        hPSC-assign-h1.pdf
        hPSC-assign-h1.png
        ml_eval.pdf
        ml_pred.pdf
        mouse_on_human.pdf
        mouse_on_human.png
        mouse_on_human_norm.pdf
        mouse_on_human_norm.png
        oscope-fraction-test-cc-only.pdf
        oscope-fraction-test-cc-only.png
        oscope-fraction-test.pdf
        oscope-fraction-test.png
        oscope-fraction-test_old1.png
        oscope-fraction-test_old2.png
        oscope-pca.pdf
        oscope-pca.png
        oscope-phase-distribution.pdf
        oscope-phase-distribution.png
        prediction-mESC-on-hESC.png

    extension/
        networkx.pdf
        networkx.png
        pairs_ematb6142.pdf
        pairs_ematb6142.png
        rfp_ematb6142.pdf
        rfp_ematb6142.png
        rf_bulk.pdf
        rf_bulk.png
        triplets.pdf

    general/
        logo1.jpg
        logo2.jpg
        logos_combined.PNG

notebooks/

    2. Python reimplementation/
        2.3 Differences in code - Python.ipynb
        2.3 Differences in code - R.ipynb

    3. Evaluation/
        3.2 Mouse pairs on human dataset.ipynb
        3.3 Internal cross validation.ipynb
        3.4.1.1 Bulk - GSE53481.ipynb
        3.4.1.2 Bulk - GSE71456.ipynb
        3.4.2 Single cell - EMATB6142.ipynb

    4. Application/
        4.1 EBV.ipynb
        4.2 E-MTAB-3929.ipynb

    5. Extension/
        5.1 Random forest on pairs.ipynb
        5.2 Pairs network.ipynb
        5.2.1 Weighted Pairs.ipynb
        5.2.2. Weighted Triplets.ipynb

The PyPairs implementation is also available via pip:

$ pip install pypairs

And Github:

https://github.com/rfechtner/pypairs

For questions please write me at:

ronfechtner@gmail.com