Toolkit for analysis and identification of cell types from heterogeneous single cell RNA-seq data
Project description
Digital Cell Sorter
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 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
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:
- 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.
Examples:
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 |
... | ... | ... | ... |
- 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'
.
Examples:
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 | |
... | ... | ... | ... | ... |
Pandas DataFrame
whereaxis 0
is genes andaxis 1
are cells. If the are batched in the data then the index ofaxis 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.
Examples:
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']))
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.
Examples:
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']))
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.
Example:
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 | ... |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
Human.MitoCarta2.0.csv
: An csv
spreadsheet with human mitochondrial genes, created within work
MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins
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:
- Pre-preprocessing
- Quality control
- Batch effects correction
- Cells anomaly score evaluation
- Dimensionality reduction
- Clustering
- Annotating cell types
- Vizualization
- 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.
The visualization tools include:
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
getIndividualGeneExpressionPlot()
: 2D layout colored by individual gene's expression
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
makeHistogramNullDistributionPlot()
: null distribution for each cluster and each cell type illustrating the "machinery" of the Digital Cell Sorter
makeQualityControlHistogramPlot()
: Quality control histogram plots
makeProjectionPlot()
: 2D layout colored by number of unique genes expressed, number of counts measured, and a faraction of mitochondrial genes..
Effect of batch correction demostrated on combining BM1, BM2, BM3 and processing the data jointly without (left) and with (right) batch correction option:
makeStackedBarplot()
: plot with fractions of various cell types
makeSankeyDiagram()
: river plot to compare various results
getAnomalyScoresPlot()
: plot with anomaly scores per cell
Calculate and plot anomaly scores for an arbitrary cell type or cluster:
getIndividualGeneTtestPlot()
: Produce heatmap plot of t-test p-Values calculated gene-pair-wise from the annotated clusters
makePlotOfNewMarkers()
: genes significantly expressed in the annotated cell types
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:
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for DigitalCellSorter-1.3.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4191ce36ffffa25da81757b7b469a0adc78cc8d2295617d506960f38b1a9281d |
|
MD5 | f1e07cbc3fd7ad86a3c0121e5477876d |
|
BLAKE2b-256 | 9061bedf96cd4268413eb6ef8fe18a531d8bc833e1bbed1b44822d4d20ad350a |