Metadata-Version: 2.1
Name: metacells
Version: 0.5
Summary: Single-cell RNA Sequencing Analysis
Home-page: https://github.com/tanaylab/metacells.git
Author: Oren Ben-Kiki
Author-email: oren@ben-kiki.org
License: MIT
Description: Metacells - Single-cell RNA Sequencing Analysis
        ===============================================
        
        .. image:: https://travis-ci.org/tanaylab/metacells.svg?branch=master
            :target: https://travis-ci.org/tanaylab/metacells
            :alt: Build Status
        
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            :target: https://metacells.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        
        The metacells package implements the improved metacell algorithm [1]_ for single-cell RNA sequencing
        (scRNA-seq) data analysis within the `scipy https://www.scipy.org/` framework. The original metacell
        algorithm [2]_ was implemented in R. The python package contains various algorithmic improvements
        and is scalable for larger data sets (millions of cells).
        
        Metacell Analysis
        =================
        
        Naively, scRNA_seq data is a set of cell profiles, where for each one, for each gene, we get a count
        of the mRNA molecules that existed in the cell for that gene. This serves as an indicator of how
        "expressed" or "active" the gene is.
        
        As in any real world technology, the raw data may suffer from technical artifacts (counting the
        molecules of two cells in one profile, counting the molecules from a ruptured cells, counting only
        the molecules from the cell nucleus, etc.). This requires pruning the raw data to exclude such
        artifacts.
        
        The current technology scRNA-seq data is also very sparse (typically <<10% the RNA molecules are
        counted). This introduces large sampling variance on top of the original signal, which itself
        contains significant inherent biological noise.
        
        Analyzing scRNA-seq data therefore requires processing the profiles in bulk. Classically, this has
        been done by directly clustering the cells using various methods.
        
        In contrast, the metacell approach groups together profiles of the "same" biological state into
        groups of cells of the "same" biological state, with the *minimal* number of profiles needed for
        computing robust statistics (in particular, mean gene expression). Each such group is a single
        "metacell".
        
        By summing profiles of cells of the "same" state together, each metacell greatly reduces the
        sampling variance, and provides a more robust estimation of the transcription state. Note a metacell
        is *not* a cell type (multiple metacells may belong to the same "type", or even have the "same"
        state, if the data sufficiently over-samples this state). Also, a metacell is *not* a parametric
        model of the cell state. It is merely a more robust description of some cell state.
        
        The metacells should therefore be further analyzed as if they were cells, using additional methods
        to classify cell types, detect cell trajectories and/or lineage, build parametric models for cell
        behavior, etc. Using metacells as input for such analysis techniques should benefit both from the
        more robust, less noisy input; and also from the (~100-fold) reduction in the number of cells to
        analyze when dealing with large data (e.g. analyzing millions of individual cells).
        
        Installation
        ============
        
        In short: ``pip install metacells``. If you do not have ``sudo`` privileges, you might need to ``pip
        install --user metacells``. Note that ``metacells`` requires many "heavy" dependencies, most notably
        ``numpy``, ``pandas``, ``scipy``, ``scanpy``, which ``pip`` should automatically install for you.
        
        Note that ``metacells`` only runs natively on Linux and MacOS. To run it on a Windows computer, you
        must activate `Windows Subsystem for Linux <https://docs.microsoft.com/en-us/windows/wsl>`_ and
        install ``metacells`` within it.
        
        The metacells package contains extensions written in C++. The ``metacells`` distribution provides
        pre-compiled Python wheels for both Linux and MacOS, so installing it using ``pip`` should not
        require a C++ compilation step.
        
        Note that for X86 CPUs, these pre-compiled wheels were built to use AVX2, and will not work on older
        CPUs which are limited to SSE. Also, these wheels will not make use of any newer instructions (such
        as AVX512), even if available. While these wheels may not the perfect match for the machine you are
        running on, they are expected to work well for most machines.
        
        To see the native capabilities of your machine, you can ``grep flags /proc/cpuinfo | head -1`` which
        will give you a long list of supported CPU features in an arbitrary order, which may include
        ``sse``, ``avx2``, ``avx512``, etc. You can therefore simply ``grep avx2 /proc/cpuinfo | head -1``
        to test whether AVX2 is/not supported by your machine.
        
        You can avoid installing the pre-compiled wheel by running ``pip install metacells
        --install-option='--native'``. This will force ``pip`` to compile the C++ extensions locally on your
        machine, optimizing for its native capabilities, whatever these may be. However, this requires you
        to have a C++ compiler installed (either ``g++`` or ``clang``), and it will take much longer to
        complete the installation.
        
        Vignettes
        =========
        
        The `generated documentation <https://metacells.readthedocs.io/en/latest>`_
        contains the following vignettes:
        `Basic Metacells Vignette <https://metacells.readthedocs.io/en/latest/Metacells_Vignette.html>`_,
        `Manual Analysis Vignette <https://metacells.readthedocs.io/en/latest/Manual_Analysis.html>`_,
        and
        `Seurat Analysis Vignette <https://metacells.readthedocs.io/en/latest/Seurat_Analysis.html>`_.
        
        You can also access their very latest version in the `Github repository
        <https://github.com/tanaylab/metacells/tree/master/sphinx>`_.
        
        References
        ==========
        
        Please cite the references appropriately in case they are used:
        
        .. [1] ORCID ProfileOren Ben-Kiki, Akhiad Bercovitch, Aviezer Lifshitz, Amos Tanay: A divide and
           conquer metacell algorithm for scalable scRNA-seq analysis.
           `10.1101/2021.08.08.453314 <https://doi.org/10.1101/2021.08.08.453314>`_
        
        .. [2] Baran, Y., Bercovich, A., Sebe-Pedros, A. et al. MetaCell: analysis of single-cell RNA-seq
           data using K-nn graph partitions. Genome Biol 20, 206 (2019).
           `10.1186/s13059-019-1812-2 <https://doi.org/10.1186/s13059-019-1812-2>`_
        
        License (MIT)
        =============
        
        Copyright © 2020, 2021 Weizmann Institute of Science
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
        associated documentation files (the "Software"), to deal in the Software without restriction,
        including without limitation the rights to use, copy, modify, merge, publish, distribute,
        sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all copies or substantial
        portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
        NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
        NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES
        OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
        CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
Provides-Extra: test
Provides-Extra: develop
