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fcmcmp-0.1.0
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مشاهده بیشترتوضیحات
A lightweight, flexible, and modern framework for annotating flow cytometry data.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fcmcmp-0.1.0 |
| نام | fcmcmp |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Kale Kundert |
| ایمیل نویسنده | kale.kundert@ucsf.edu |
| آدرس صفحه اصلی | https://github.com/kalekundert/fcmcmp |
| آدرس اینترنتی | https://pypi.org/project/fcmcmp/ |
| مجوز | MIT |
******
FCMcmp
******
The goal of FCMcmp is to make it easy to analyze flow cytometry data from
python. The first challenge in analyzing flow cytometry data is working out
which wells should be compared with each other. For example, which wells are
controls, which wells are replicates of each other, which wells contain the
conditions you're interested in, etc. This isn't usually too complicated for
individual experiments, but if you want to write analysis scripts that you can
use on all of your data, managing this metadata becomes a significant problem.
FCMcmp addresses this problem by defining a simple YAML file format that can
associate wells and plates in pretty much any way you want. When you ask
FCMcmp to parse these files, it returns a list- and dictionary-based data
structure that contains these associations, plus it automatically parses the
raw FCS data into pandas data frames.
.. image:: https://img.shields.io/pypi/v/fcmcmp.svg
:target: https://pypi.python.org/pypi/fcmcmp
.. image:: https://img.shields.io/pypi/pyversions/fcmcmp.svg
:target: https://pypi.python.org/pypi/fcmcmp
.. image:: https://img.shields.io/travis/kalekundert/fcmcmp.svg
:target: https://travis-ci.org/kalekundert/fcmcmp
.. image:: https://img.shields.io/coveralls/kalekundert/fcmcmp.svg
:target: https://coveralls.io/github/kalekundert/fcmcmp?branch=master
Installation
============
``fcmcmp`` is available on PyPI::
pip3 install fcmcmp
Only python>=3.4 is supported.
Quick Start
===========
I'll demonstrate using data you might export from running a 96-well plate on a
BD LSRII, but the library should be pretty capable of handling any directory
hierarchy::
my_plate/
96 Well - U bottom/
Specimen_001_A1_A01_001.fcs
Specimen_002_A2_A02_002.fcs
Specimen_003_A3_A03_003.fcs
...
Loading the data
~~~~~~~~~~~~~~~~
First, we need to make a YAML metadata file describing the relationships
between the wells on this plate::
# my_plate.yml
label: vaxadrin
wells:
without: [A1,A2,A3]
with: [B1,B2,B3]
---
label: vaxamaxx
wells:
without: [A1,A2,A3]
with: [C1,C2,C3]
In this example, the name of the plate directory is inferred from the name of
the YAML file. You can also explicitly specify the path to the plate directory
by adding the following header before the ``label``/``wells`` sections::
plate: path/to/my_plate
---
You can even reference wells from multiple plates in one file::
plates:
foo: path/to/foo_plate
bar: path/to/bar_plate
---
label: vaxascab
wells:
without: [foo/A1, foo/A2, foo/A3]
with: [bar/A1, bar/A2, bar/A3]
Note that the ``label`` and ``wells`` fields are required, but you can add,
remove, or rename any other field::
label: vaxa-smacks
channel: FITC-A
gating: 60%
wells:
0mM: [A1,A2,A3]
1mM: [B1,B2,B3]
5mM: [C1,C2,C3]
Once you have a YAML metadata file, you can use ``fcmcmp`` to read it::
>>> import fcmcmp, pprint
>>> experiments = fcmcmp.load_experiments('my_plate.yml')
>>> pprint.pprint(experiments)
[{'label': 'vaxadrin',
'wells': {'with': [Well(B1), Well(B2), Well(B3)],
'without': [Well(A1), Well(A2), Well(A3)]}},
{'label': 'vaxamaxx',
'wells': {'with': [Well(C1), Well(C2), Well(C3)],
'without': [Well(A1), Well(A2), Well(A3)]}}]
The data structure returned is little more than a list of dictionaries, which
should be easy to work with in pretty much any context. The wells are
represented by ``Well`` objects, which have only three attributes:
- ``Well.label``: The name used to reference the well in the YAML file.
- ``Well.data``: A ``pandas.DataFrame`` containing all the data associated
with the well, parsed using the excellent ``fcsparse`` library.
- ``Well.meta``: A dictionary containing any metadata associated with the
well, also parsed using ``fcsparse``.
Note that if you reference the same well more than once (e.g. for controls that
apply to all of your experiments), each reference is parsed separately and gets
its own copy of all the data.
Working with the data
~~~~~~~~~~~~~~~~~~~~~
Once the experiments are loaded into python as described above, ``fcmcmp``
provides a couple ways to interact with them. The first is to apply one or
more of a handful of pre-defined "processing steps"::
>>> ch = 'FITC-A', 'PE-Texas Red-A'
>>> p1 = fcmcmp.GateEarlyEvents(throwaways_secs=2)
>>> p1(experiments)
>>> p2 = fcmcmp.GateSmallCells(threshold=40, save_size_col=True)
>>> p2(experiments)
>>> p3 = fcmcmp.GateNonPositiveEvents(ch)
>>> p3(experiments)
>>> p4 = fcmcmp.LogTransformation(ch)
>>> p4(experiments)
>>> p5 = fcmcmp.KeepRelevantChannels(ch)
>>> p5(experiments)
In this example:
- ``GateEarlyEvents`` discards the first few seconds of data, which is useful
when you're using a high-throughput sampler and you suspect that cells from
the previous well are being recorded at the beginning of each well.
- ``GateSmallCells`` combines the ``FSC-A`` and ``SSC-A`` channels to estimate
how the size of each event, then discards any events below the given
percentile (40% in this example).
- ``GateNonPositiveEvents`` discards negative data on the specified channels.
I have to admit that I don't understand how "fluorescence peak area" data can
be negative, but in any case this can be important if you want to work with
the logarithm of your data, because of course you can't take the logarithm of
negative data.
- ``LogTransform`` takes the logarithm of the data in the specified channels.
This is a very standard processing step for fluorescent channels.
- ``KeepRelevantChannels`` discards all the data for any channels that aren't
explicitly listed. This is mostly useful for when you're printing out data
to the terminal and don't want to be distracted by channels you collected but
aren't interested in at the moment.
Instead of calling each processing step individually, you can also use the
``run_all_processing_steps()`` function to call them all at once. If you do
this, you don't even need to make a variable for each step::
>>> fcmcmp.GateEarlyEvents(throwaways_secs=2)
>>> fcmcmp.GateSmallCells(threshold=40, save_size_col=True)
>>> fcmcmp.GateNonPositiveEvents(ch)
>>> fcmcmp.LogTransformation(ch)
>>> fcmcmp.KeepRelevantChannels(ch)
>>> fcmcmp.run_all_processing_steps()
You can also write your own processing steps by inheriting from either
``ProcessingStep`` or ``GatingStep`` and reimplementing the proper methods.
``ProcessingStep`` is for general transformations and has two virtual methods:
``process_experiment()`` and ``process_well()``. The former is called once for
each experiment and should transform that experiment in place. The latter is
called once for each well and can either modify the well in place (and return
None) or return the processed data, which will overwrite the original data.
``GatingStep`` is specifically for transformations regarding which data points
to keep and which to throw out. It is itself a ``ProcessingStep``, but it has
a different virtual method(): ``gate()``. This method is called on each well
and should return a boolean numpy array. Those indices that are ``False`` will
be thrown out, those that are ``True`` will be kept.
The second way to interact with the experiments is to use the ``yield_wells()``
and ``yield_unique_wells()`` functions. These are both `generators`__ which
iterate through all of your experiments and yield each well one at a time. The
purpose of these functions is to make the nested ``experiments`` data structure
seem more like a flat list::
>>> for experiment, condition, well in fcmcmp.yield_wells(experiments):
>>> print(experiment, condition, well)
Both functions take an optional keyword argument. If given, only wells with a
matching experiment label, condition, or well label will be returned. The only
difference between ``yield_wells()`` and ``yield_unique_wells()`` is that the
former won't yield the same well twice. This is important because the same
well can certainly be included in many different experiments.
__ https://jeffknupp.com/blog/2013/04/07/improve-your-python-yield-and-generators-explained/
Bugs and new features
=====================
Use the GitHub issue tracker if you find any bugs or would like to see any new
features. I'm also very open to pull requests.
نحوه نصب
نصب پکیج whl fcmcmp-0.1.0:
pip install fcmcmp-0.1.0.whl
نصب پکیج tar.gz fcmcmp-0.1.0:
pip install fcmcmp-0.1.0.tar.gz