metaclean3 0.1.0

Removes anomalous events from flow cytometry standard (FCS) files.

MetaClean3.0

MetaClean3.0 cleans/removes low-quality events in FCS (flow cytometry standard) data detected during irregular flow.

This method is developed for and funded by Metafora biosystem's MetaFlow platform.

Installation

Pre-requisites:

• Python 3
• PyTorch and cuML (Optional, for processing on GPU)

To install Python 3 on Linux:

metaclean3_public$ sudo apt install python3
metaclean3_public$ sudo apt install python3-pip

Install this package:

Install this package via PyPI:

metaclean3_public$ pip install metaclean3

OR install this package locally:

1. Download this repository.
2. Build and install the package; on Linux, go to the repository directory:

metaclean3_public$ python -m pip install pip-tools
metaclean3_public$ pip-compile pyproject.toml # generate requirements.txt
metaclean3_public$ pip-sync # install dependencies from generate requirements.txt
metaclean3_public$ pip install .

Usage

See API here.

MetaClean3.0 contains three classes, to of which you will directly interact with:

Once you have installed metaclean3, in Python:

1. Load your .fcs file using the fcsparser package. (Optional) We recommend that you compensate and transform your file before applying MetaClean3.0 for optimal results.

import fcsparser

fcs_file_local = '/path/to/fcs_file.fcs'
meta, data = fcsparser.parse(fcs_file_local, reformat_meta=False)

2. Instantiate an FCSfile class f that will help format your .fcs file for MetaClean3.0. We strongly recommend users to compensate (and optionally transform) the .fcs file before applying MetaClean3.0.

from metaclean3 import FCSfile
from metaclean3.utils import (
    get_timestep,
    get_spillover_raw,
    apply_compensation_matrix
)

# compensate .fcs file
sm = get_spillover_raw(meta=meta, dat_columns=data.columns)
if not (sm is None):
    data[sm.columns] = apply_compensation_matrix(data[sm.columns], sm)

# instantiate FCSfile
f = FCSfile(data=data, time_step=get_timestep(meta))

3. Run MetaClean3.0 on your file f.

from metaclean3 import MetaCleanFCS

d = MetaCleanFCS().apply(fcs=f)

If you wish, MetaClean3.0 can save cleaning process plots to a directory of your choice:

png_dir = '/path/to/save/png/plots/in'
d = MetaCleanFCS(png_dir=png_dir).apply(fcs=f)

The output pandas.DataFrame, d contains additional columns:

• clean_keep: The boolean column indicating which events to keep (True) or remove (False). This is the final result you are looking for!
• bin: The bin label for each event.
• outlier_keep: The boolean column indicating which events were deemed as outliers (False).
• val_*: The feature values extracted based on the chosen fluorescent measurement.

General-purpose cleaning

If you already have your way of extracting features and binning your .fcs data and you just want to execute the cleaning portion of MetaClean3.0 on a pandas.DataFrame, binned_data, you can do so directly with MetaClean:

from metaclean3 import MetaClean

d = MetaClean().clean(data=binned_data) # all columns will be used.

Fine-tuning MetaClean3.0

For most cases, we recommend using default settings. However, if there are cases when you want to fine-tune results, some common arguments you can change are listed below. See attributes in the MetaCleanFCS and MetaClean for more details.

FCSfile arguments control how events are binned

See FCSfile API here.

min_bin_size and max_bin_size (default: 2000 and 10000): The minimum and maximum number of bins allowed. If you have a large file with more than ten million events and you want MetaClean3.0 results to be more precise, you can increase max_bin_size. Adjust with moderation.

f = FCSfile(data=data, time_step=get_timestep(meta), min_bin_size=2000, max_bin_size=10000)

MetaCleanFCS arguments control feature selection and generation

See MetaCleanFCS API here.

fluo_chans_no (default: 4): The number of fluorescent measurements to consider.

d = MetaCleanFCS(fluo_chans_no=4).apply(fcs=f)

fluo_chans_clean (default: None): If you already know what fluorescent measurements you want MetaClean3.0 to refer to when determining what events to keep, you can list them here as a string list. To see what fluorescent measurements are in your file, refer to your FCSfile instance, f.fluo_chans.

d = MetaCleanFCS(fluo_chans_clean=['FL1', 'FL2']).apply(fcs=f)

candidate_chans_type (default: fluo): The type of channels to use for cleaning i.e. fluo (fluorescent), phys (physical morphology), or all.

d = MetaCleanFCS(candidate_chans_type=fluo).apply(fcs=f)

rm_outliers (default: 'all'): MetaClean3.0 detects and removes outliers so they do not skew MetaClean3.0's judgement when removing low-quality events. If, in the final results, you do not want to remove all of these outliers, you can specify to keep them by setting rm_outliers to 'all'. If you want to keep some of the less outlying events, set this parameter to 'some' and if you want to remove all outliers, set this parameter to 'none'.

d = MetaCleanFCS(rm_outliers='all').apply(fcs=f)

n_cores (default: -1): The number of cores to use while calculating the density feature. Set to -1 to use all cores.

d = MetaCleanFCS(n_cores=-1).apply(fcs=f)

MetaClean arguments control degree of leniency

See MetaClean API here.

min_ref_percent (default: 0.4 40%; range: [0, 1]): The minimum percentage of bins they want MetaClean3.0 to keep.

d = MetaCleanFCS(min_ref_percent=0.4).apply(fcs=f)

min_ref_percent_to_keep (default: 0.4 40%; range: [0, 1]): If non-reference segments contain at least min_ref_percent_to_keep bins, we keep it even if it differs from the longest reference segment.

d = MetaCleanFCS(min_ref_percent_to_keep=0.4).apply(fcs=f)

p_thres (default: 0.05; range: [0, 1]): Adjacent event segments (along time) that do not have significantly different values (i.e. p-value > p_thres) are merged. If p_thres is small, MetaClean3.0 will remove fewer events (i.e. be more lenient).

d = MetaCleanFCS(p_thres=0.05).apply(fcs=f)

percent_shifts (default: [0.15, 0.2, 0.25, 0.3, 0.35, 0.4]; range: [0, 1]): Adjacent event segments (along time) that have quantile values within a specified range of each other are merged. percent_diff=0.05 indicates that the quantiles to be tested are 5s% and 95%. percent_shifts=[0.15, 0.2] indicates that MetaClean3.0 will test if the segments have quantile values within a 15% and 20% range of each other, whiever yeilds results that achieve a higher silhouette score. Small percent_shift values mean that MetaClean3.0 will remove fewer events (i.e. be more lenient).

d = MetaCleanFCS(percent_shift=[0.15]).apply(fcs=f)

API/Documentation

See API here.

This repository uses Sphinx to generate documentation contained in the docs. To generate documentation locally on Linux:

1. install Sphinx for Python:

metaclean3_public$ apt-get install python3-sphinx
metaclean3_public$ pip3 install -r docs/source/requirements.txt
metaclean3_public$ # sphinx-quickstart docs

2. Generate the HTML documentation:

metaclean3_public$ sphinx-build -M html docs/source/ docs/build/

Now you can open the front page in your web browser: docs/build/html/index.html.

Development

Unit testing: put test .fcs files in tests/data before starting.

metaclean3_public$ python3 -m unittest discover tests

Build and compile package:

metaclean3_public$ python -m pip install pip-tools
metaclean3_public$ pip-compile --extra dev pyproject.toml # generate requirements.txt
metaclean3_public$ pip install -r requirements.txt # test installation of dependencies
metaclean3_public$ sudo python3 -m build # builds package into a zip
metaclean3_public$ twine check dist/* # check if package description renders on PyPI

Upload to TestPyPI:

metaclean3_public$ twine upload -r testpypi dist/* # upload
metaclean3_public$ python -m pip install -i https://test.pypi.org/simple metaclean3 # test install

Upload package to PyPI:

metaclean3_public$ twine upload dist/*
metaclean3_public$ pip install metaclean3 # test install

Support

See the issues section of this repository.

Citation

If this package helped you in any way please cite MetaClean3.0's paper:

(TBP)