PySCUBA 0.1.1
Python for Single-cell Clustering Using Bifurcation Analysis
License: MIT License
Keywords: bioinformatics biology clustering cytometry gap-statistics genomics machine-learning pattern-recognition PCR principal-curve qPCR RNASeq single-cell time-series unsupervised-learning

PySCUBA
=======

PySCUBA stands for "Python for Single-cell Clustering Using Bifurcation
Analysis".

PySCUBA is a novel computational method for extracting lineage
relationships from single-cell genomics data, and modeling the dynamic
changes associated with cell differentiation. PySCUBA draws from
techniques in nonlinear dynamics and knowledge of stochastic
differential equations to provide a systematic framework for modeling
complex processes involving multi-lineage specifications.

There is a MATLAB implementation of this method. However, PySCUBA is a
complete overhaul and redesign, where some bugs, mathematical
inconsistencies and improper handling of outlier cases have been taken
care of. PySCUBA also comes with a GUI, written using the Python wrapper
for the Qt framework.

PySCUBA is open source and can be used without the high licensing fees
that were hampering some of our colleagues. Furthermore, PySCUBA is a
much faster and more efficient way of handling the computations and data
processing underlying the corresponding computational biology method. On
a benchmark comparison, it was found that where the MATLAB code took
about 20 minutes to complete, PySCUBA handles the same task in about 30
seconds.

Installation
------------

PySCUBA is written in Python 2.7. It relies on the following libraries
and APIs: \* ``matplolib`` (1.4.3 or ulterior version) \* ``numpy``
(1.9.0 or later) \* ``Pillow`` (3.2.0 or later) \* ``PyQt4`` (at least
version 4.11.4) \* ``python-igraph`` (version>=0.7.1) \* ``rpy2`` (2.8.1
or later) \* ``scipy`` (0.17.0 or later) \* ``setuptools``
(version>=21.0.0) \* ``sklearn`` (version>=0.17.1) \* ``Wand`` (version
0.4.3 or subsequent)

Most of those dependencies should be automatically resolved during an
installation with ``pip``, by \* starting a terminal; \* running
``$ pip install PySCUBA``.

Nonetheless, please ensure all those prerequisites have been met before
proceeding any further. In particular, ``PyQt`` might have to be
installed manually. In addition, note that you might need to run this
using ``sudo`` depending on your Python installation.

Alternatively, you can also get PySCUBA from source by downloading a
tarball or zip archive from the corresponding GitHub page. Once you have
downloaded and unpacked the source, navigate into the root source
directory and run:

``$ python setup.py install``

Usage
-----

In your terminal, run

``$ PySCUBA``

upon which a graphical user interface should pop up.

Select the file to process and choose the relevant data type and other
such parameters. Detailed explanations for each of those options can be
obtained by maintaining your cursor on a particular button; this
includes format specifications for your dataset.

After your data has been subjected to various iterations of
gap-statistics and penalized maximum likelihood estimations of the
parameters of a Fokker-Planck potential, you will be prompted to choose
various output files to display within the PySCUBA GUI. You can scroll
up and down the display window, as well as zoom in and out.

Example
-------

A fully-functional, annotated example demonstrating a standard usage
PySCUBA is provided herewith.

First of all, in a Python session, let us import a few modules.
``Pandas`` will prove quite convenient to fetch our example dataset:

::

    from os import getcwd, path
    import pandas as pd

The dataset in question is some qPCR data from Deng et al., "Single-cell
RNA-seq reveals dynamic, random monoallelic gene expression in mammalian
cells." Science. 2014 Jan 10;343(6167):193-6. It is accessible from a
particular GitHub repository, whose url appears below. We want the
content of that file, not its full GitHub view, which is why we use the
``Raw`` link to that repository and explains the lack of any ``blob/``
in its url path:

::

    url = 'https://raw.githubusercontent.com/gcyuan/SCUBA/master/sample_data/guo2010/guo2010Data.txt'
    df = pd.read_csv(url, delimiter='\t')

Check that nothing untoward occurred by typing in

::

    df.head()

We are now going to write this dataframe to a tab-separated \*.txt file
in your current working directory:

::

    df.to_csv(path.join(getcwd(), 'super_duper_data.txt'), sep='\t', index=False)

In your terminal, we are now ready to launch the PySCUBA graphical user
interface, which can be launched from any directory (not necessarily the
one holding ``super_duper_data.txt``):

::

    $ PySCUBA

Please refer to PySCUBA's GitHub page for screenshots illustrating how the PySCUBA GUI looks at start time, how to select ``super_duper_data.txt`` for processing and,
once the dust settles down, how to select files for display and further
investigations.

Attribution
-----------

If you find PySCUBA useful in your research, please cite its GitHub
repository:

Giecold G, PySCUBA, (2016), GitHub repository,
https://github.com/GGiecold/PySCUBA

The respective BibTex entry is

::

    @misc{Giecold2016,
      author = {Giecold, G.},
      title = {PySCUBA},
      year = {2016},
      publisher = {GitHub},
      journal = {GitHub repository},
      howpublished = {\url{https://github.com/GGiecold/PySCUBA}},
      commit = {8ee6a08de15decdcdaf7d877888ae783832d80f2}
    }

Besides, please cite the following reference as well:

Marco E, Karp RL, Guo G, Robson P, Hart AH, Trippa L, Yuan GC. (2014)
"Bifurcation analysis of single-cell gene expression data reveals
epigenetic landscape". Proceedings of the National Acacdemy of Sciences,
111, E5643-E5650.

License
-------

Copyright 2016-2021 Gregory Giecold.

PySCUBA is free software made available under the MIT License. For
details see the LICENSE file.