Metadata-Version: 2.1
Name: DigitalCellSorter
Version: 1.3.0
Summary: Toolkit for analysis and identification of cell types from heterogeneous single cell RNA-seq data
Home-page: https://github.com/sdomanskyi/DigitalCellSorter
Author: S. Domanskyi , A. Szedlak, N. T Hawkins, J. Wang, G. Paternostro, C. Piermarocchi
Author-email: s.domanskyi@gmail.com
License: MIT License
Download-URL: https://github.com/sdomanskyi/DigitalCellSorter/archive/1.3.0.tar.gz
Description: # Digital Cell Sorter
        
        [![DOI](https://badge.fury.io/gh/sdomanskyi%2FDigitalCellSorter.svg)](https://badge.fury.io/gh/sdomanskyi%2FDigitalCellSorter)
        [![DOI](https://badge.fury.io/py/DigitalCellSorter.svg)](https://pypi.org/project/DigitalCellSorter)
        [![DOI](https://readthedocs.org/projects/digital-cell-sorter/badge/?version=latest)](https://digital-cell-sorter.readthedocs.io/en/latest/?badge=latest)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3538306.svg)](https://doi.org/10.5281/zenodo.3538306) 
        
        Digital Cell Sorter (DCS): a single cell RNA-seq analysis toolkit for clustering, cell type identification, and anomaly detection.
        
        > **Note:** We are currently preparing a manuscript describing the toolkit located this repository.
        > If you want to access the package detailed in our latest publication of Polled Digital Cell Sorter
        > go to https://zenodo.org/record/2603265 and download the package (v1.1).
        
        
        > **The latest publication describing the methodology of cell types identification:**
        > [Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters](
        > https://doi.org/10.1186/s12859-019-2951-x 
        > "Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters")
        > Sergii Domanskyi, Anthony Szedlak, Nathaniel T Hawkins, Jiayin Wang, Giovanni Paternostro & Carlo Piermarocchi, 
        > *BMC Bioinformatics* volume 20, Article number: 369 (**2019**)
        
        
        The documentation is available at https://digital-cell-sorter.readthedocs.io/.
        
        - [Getting Started](#getting-started)
          * [Prerequisites](#prerequisites)
          * [Loading the package](#loading-the-package)
          * [Gene Expression Data Format](#gene-expression-data-format)
          * [Other Data](#other-data)
        - [Functionality](#functionality)
          * [Overall](#overall)
          * [Visualization](#visualization)
        - [Demo](#demo)
          * [Usage](#usage)
            + [Main cell types](#main-cell-types)
            + [Cell sub-types](#cell-sub-types)
          * [Output](#output)
        
        ## Getting Started
        
        These instructions will get you a copy of the project up and running on your machine for data analysis, development or testing purposes.
        
        ### Prerequisites
        
        The code runs in Python >= 3.7 environment. 
        
        It is highly recommended to install Anaconda.
        Installers are available at https://www.anaconda.com/distribution/
        
        It uses packages ```numpy```, ```pandas```, ```matplotlib```, ```scikit-learn```, ```scipy```, 
        ```mygene```, ```fftw```, ```pynndescent```, ```networkx```, ```python-louvain```, ```fitsne```, 
        ```adjustText```, ```phate```, ```umap-learn```, ```plotly```
        and a few other standard Python packages. Most of these packages are installed with installation of the 
        latest release of ```DigitalCellSorter```:
        
        	pip install DigitalCellSorter
        
        Alternatively, you can install this module directly from GitHub using:
        
        	pip install git+https://github.com/sdomanskyi/DigitalCellSorter
        
        Also one can create a local copy of this project for development purposes by running:
        
        	git clone https://github.com/sdomanskyi/DigitalCellSorter
        
        After cloning the package can also be installed from the cloned directory by:
        
        	python setup.py install
        
        To install ```fftw``` from the ```conda-forge``` channel add ```conda-forge``` to your channels.
        Once the conda-forge channel has been enabled, ```fftw``` can be installed as follows:
        
        	conda config --add channels conda-forge
        	conda install fftw
        
        To use Sankey diagrams that are part of Digital Cell Sorter install ```orca```:
        
            conda install -c plotly plotly-orca
        
        ### Loading the package
        
        In your script import the package:
        
        	import DigitalCellSorter
        
        Create an instance of class ```DigitalCellSorter```. Here, for simplicity, we use Default parameter values:
        
        	DCS = DigitalCellSorter.DigitalCellSorter()
        
        During the initialization a number of parameters can be specified. For detailed list see documentation.
        Many of these parameters are transfered to DCS attributes thus can be modified after initialization using, e.g.:
        
        	DCS.toggleMakeStackedBarplot = False
        
        
        
        ### Gene Expression Data Format
        
        The input gene expression data is expected in one of the following formats:
        
        1. Spreadsheet of comma-separated values ```csv``` containing condensed matrix in a form ```('cell', 'gene', 'expr')```. 
        If there are batches in the data the matrix has to be of the form ```('batch', 'cell', 'gene', 'expr')```. Columns order can be arbitrary.
        
        <details closed><summary>Examples:</summary><p>
        
        | cell | gene | expr |
        |------|------|------|
        | C1   | G1   | 3    |
        | C1   | G2   | 2    |
        | C1   | G3   | 1    |
        | C2   | G1   | 1    |
        | C2   | G4   | 5    |
        | ...  | ...  | ...  |
        
        or:
        
        | batch  | cell | gene | expr |
        |--------|------|------|------|
        | batch0 | C1   | G1   | 3    |
        | batch0 | C1   | G2   | 2    |
        | batch0 | C1   | G3   | 1    |
        | batch1 | C2   | G1   | 1    |
        | batch1 | C2   | G4   | 5    |
        | ...    | ...  | ...  | ...  |
        
        </p></details>
        
        
        2. Spreadsheet of comma-separated values ```csv``` where rows are genes, columns are cells with gene expression counts.
        If there are batches in the data the spreadsheet the first row should be ```'batch'``` and the second ```'cell'```.
        
        <details closed><summary>Examples:</summary><p>
        
        | cell  | C1     | C2     | C3     | C4     |
        |-------|--------|--------|--------|--------|
        | G1    |        | 3      | 1      | 7      |
        | G2    | 2      | 2      |        | 2      |
        | G3    | 3      | 1      |        | 5      |
        | G4    | 10     |        | 5      | 4      |
        | ...   | ...    | ...    | ...    | ...    |
        
        or:
        
        | batch | batch0 | batch0 | batch1 | batch1 |
        |-------|--------|--------|--------|--------|
        | cell  | C1     | C2     | C3     | C4     |
        | G1    |        | 3      | 1      | 7      |
        | G2    | 2      | 2      |        | 2      |
        | G3    | 3      | 1      |        | 5      |
        | G4    | 10     |        | 5      | 4      |
        | ...   | ...    | ...    | ...    | ...    |
        
        </p></details>
        
        3. ```Pandas DataFrame``` where ```axis 0``` is genes and ```axis 1``` are cells.
        If the are batched in the data then the index of ```axis 1``` should have two levels, e.g. ```('batch', 'cell')```, 
        with the first level indicating patient, batch or expreriment where that cell was sequenced, and the
        second level containing cell barcodes for identification.
        
        <details closed><summary>Examples:</summary><p>
        
            df = pd.DataFrame(data=[[2,np.nan],[3,8],[3,5],[np.nan,1]], 
                              index=['G1','G2','G3','G4'], 
                              columns=pd.MultiIndex.from_arrays([['batch0','batch1'],['C1','C2']], names=['batch', 'cell']))    
        
        
        </p></details>
        
        4. ```Pandas Series ``` where index should have two levels, e.g. ```('cell', 'gene')```. If there are batched in the data
        the first level should be indicating patient, batch or expreriment where that cell was sequenced, the second level cell barcodes for 
        identification and the third level gene names.
        
        <details closed><summary>Examples:</summary><p>
        
            se = pd.Series(data=[1,8,3,5,5], 
                           index=pd.MultiIndex.from_arrays([['batch0','batch0','batch1','batch1','batch1'],
                                                            ['C1','C1','C1','C2','C2'],
                                                            ['G1','G2','G3','G1','G4']], names=['batch', 'cell', 'gene']))
        
        
        </p></details>
        
        Any of the data types outlined above need to be prepared/validated with a function ```prepare()```. 
        Let us demonstrate this on the input of type 1:
        
        	df_expr = DCS.prepare('data/testData/dataFileCondensedWithBatches.tsv')
        
        ### Other Data
        
        ```markersDCS.xlsx```: An excel book with marker data. Rows are markers and columns are cell types. 
        '1' means that the gene is a marker for that cell type, '-1' means that this gene is not expressed in this cell type, and '0' otherwise.
        This gene marker file included in the package is used by Default. 
        If you use your own file it has to be prepared in the same format (including the two-line header). Note that only the first worksheet will be read,
        and its name can be arbitrary. The first column should contain gene names. The second row should contain cell types, and the first row how 
        those cell types are grouped. If any of the cell types need to be skipped, have "NA" in the corresponding cell of the first row of that cell type.
        
        <details closed><summary>Example:</summary><p>
        
        |A       |B            |C             |D           |E          |F                |G                         |H                           |I                        |J                         |K                  |L               |M                 |...      |
        |--------|-------------|--------------|------------|-----------|-----------------|--------------------------|----------------------------|-------------------------|--------------------------|-------------------|----------------|------------------|---------|
        |        |B cells      |B cells       |B cells     |T cells    |T cells          |T cells                   |T cells                     |T cells                  |T cells                   |T cells            |NK cells        |NK cells          |...      |
        |Marker  |B cells naive|B cells memory|Plasma cells|T cells CD8|T cells CD4 naive|T cells CD4 memory resting|T cells CD4 memory activated|T cells follicular helper|T cells regulatory (Tregs)|T cells gamma delta|NK cells resting|NK cells activated|...      |
        |ABCB4   |1            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ABCB9   |0            |0             |1           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ACAP1   |0            |0             |0           |0          |1                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ACHE    |0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ACP5    |0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ADAM28  |1            |1             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ADAMDEC1|0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ADAMTS3 |0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ADRB2   |0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |AIF1    |0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |AIM2    |0            |1             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ALOX15  |0            |0             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ALOX5   |0            |1             |0           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |AMPD1   |0            |0             |1           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |ANGPT4  |0            |0             |1           |0          |0                |0                         |0                           |0                        |0                         |0                  |0               |0                 |...      |
        |...     |...          |...           |...         |...        |...              |...                       |...                         |...                      |...                       |...                |...             |...               |...      |
        
        </p></details>
        
        ```Human.MitoCarta2.0.csv```: An ```csv``` spreadsheet with human mitochondrial genes, created within work 
        [MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins](https://doi.org/10.1093/nar/gkv1003 "MitoCarta2.0")
        Sarah E. Calvo, Karl R. Clauser, Vamsi K. Mootha, *Nucleic Acids Research*, Volume 44, Issue D1, 4 January 2016.
        
        
        ## Functionality
        
        ### Overall
        
        The main class, DigitalCellSorter, includes tools for:
        
          1. **Pre-preprocessing**
          2. **Quality control**
          3. **Batch effects correction**
          4. **Cells anomaly score evaluation**
          4. **Dimensionality reduction**
          5. **Clustering**
          6. **Annotating cell types**
          7. **Vizualization**  
          8. **Post-processing**.
        
        
        ### Visualization
        
        Function ```visualize()``` will produce most of the necessary files for post-analysis of the data. 
        
        See examples of the visualization tools below.
        
        
        <details closed><summary>The visualization tools include:</summary><p>
         
        - ```makeMarkerExpressionPlot()```: a heatmap that shows all markers and their expression levels in the clusters, 
        in addition this figure contains relative (%) and absolute (cell counts) cluster sizes
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_marker_expression.png?raw=true" width="1000"/>
        </p>
        
        - ```getIndividualGeneExpressionPlot()```:  2D layout colored by individual gene's expression
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/marker_subplots/BM1_CD19_(B4_CVID3_CD19).png?raw=true" width="400"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/marker_subplots/BM1_CD33_(SIGLEC-3_CD33_p67_SIGLEC3).png?raw=true" width="400"/>
        </p>
        
        - ```makeVotingResultsMatrixPlot()```: z-scores of the voting results for each input cell type and each cluster, 
        in addition this figure contains relative (%) and absolute (cell counts) cluster sizes
        
        <p align="middle">
         <img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_scores_matrix.png?raw=true" height="700"/>
        </p>
        
        - ```makeHistogramNullDistributionPlot()```: null distribution for each cluster and each cell type illustrating 
        the "machinery" of the Digital Cell Sorter
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_null_distributions.png?raw=true" width="800"/>
        </p>
        
        - ```makeQualityControlHistogramPlot()```: Quality control histogram plots
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/QC_plots/BM1_number_of_genes_histogram.png?raw=true" width="250"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/QC_plots/BM1_count_depth_histogram.png?raw=true" width="250"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/QC_plots/BM1_fraction_of_mitochondrialGenes_histogram.png?raw=true" width="250"/>
        </p>
        
        - ```makeProjectionPlot()```: 2D layout colored by number of unique genes expressed, 
        number of counts measured, and a faraction of mitochondrial genes..
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_number_of_genes.png?raw=true" width="250"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_count_depth.png?raw=true" width="250"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_fraction_of_mitochondrialGenes.png?raw=true" width="250"/>
        </p>
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_is_quality_cell.png?raw=true" width="500"/>
        </p>
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_clusters.png?raw=true" width="375"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_patients.png?raw=true" width="375"/>
        </p>
        
        Effect of batch correction demostrated on combining BM1, BM2, BM3 and processing the data jointly without (left) and with (right) batch correction option:
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/BM123_no_corr_clusters_by_patients.png?raw=true" width="375"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/BM123_with_corr_clusters_by_patients.png?raw=true" width="375"/>
        </p>
        
        - ```makeStackedBarplot()```: plot with fractions of various cell types
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_clusters_annotated.png?raw=true" width="500"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_subclustering_stacked_barplot_BM1.png?raw=true" height="500"/>
        </p>
        
        
        - ```makeSankeyDiagram()```: river plot to compare various results 
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/Sankey_example.png?raw=true" width="800"/>
        </p>
        
        - ```getAnomalyScoresPlot()```: plot with anomaly scores per cell
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_anomaly_score_All.png?raw=true" width="750"/>
        </p>
        
        Calculate and plot anomaly scores for an arbitrary cell type or cluster:
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_anomaly_score_B_cells.png?raw=true" width="250"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_anomaly_score_T_cells.png?raw=true" width="250"/>
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_clusters_by_anomaly_score_cluster_7.0.0.png?raw=true" width="250"/>
        </p>
        
        
        - ```getIndividualGeneTtestPlot()```: Produce heatmap plot of t-test p-Values calculated gene-pair-wise
                from the annotated clusters
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_ttest_CD4_(CD4_CD4mut).png?raw=true" width="500"/>
        </p>
        
        
        - ```makePlotOfNewMarkers()```: genes significantly expressed in the annotated cell types
        
        <p align="middle">
        	<img src="https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/output/BM1/BM1_new_markers.png?raw=true" width="1000"/>
        </p>
        
        </p></details>
        
        
        ## Demo
        
        ### Usage
        
        We have made an example execution file ```demo.py``` that shows how to use ```DigitalCellSorter```.
        
        In the demo, folder ```data``` is intentionally left empty. 
        The data file (cc95ff89-2e68-4a08-a234-480eca21ce79.homo_sapiens.mtx.zip) is about 2.4Gb in size and
        will be downloaded with the ```demo.py``` script.
        
        > Previously the HCA preview data was consolidated in file ```ica_bone_marrow_h5.h5``` and downloadable  
        > from https://preview.data.humancellatlas.org/ (Raw Counts Matrix - Bone Marrow). 
        > That file was ~485Mb and containing 378000 cells from 8 bone marrow donors (BM1-BM8). 
        
        See details of the script ```demo.py``` at:
        
        > [Example walkthrough of demo.py script](https://github.com/sdomanskyi/DigitalCellSorter/blob/master/docs/examples/ "Examples")
        
        
        To execute the complete script ```demo.py``` run:
        
        	python demo.py
        
        *Note that the HCA BM1 data contains ~50000 sequenced cells, requiring more than 60Gb of RAM (we recommend to use High Performance Computers).
        If you want to run our example on a regular PC or a laptop, you can use a randomly chosen number of cells:
        
            df_expr.sample(n=5000, axis=1)
        
        
        ### Output
        
        All the output files are saved in ```output``` directory inside the directory where the ```demo.py``` script is. 
        If you specify any other directory, the results will be generetaed in it.
        If you do not provide any directory the results will appear in the root where the script was executed.
        
Keywords: single cell RNA sequencing,cell type identification,biomarkers
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
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