Package: decneo
DECNEOThis repository contains DECNEO, a Python package that provides bioinformatics utilities for analyzing single cell transcriptomics datasets. DECNEO implements in silico detection of transcriptional regulation genes. The documentation is available at Read the Docs:https://decneo.readthedocs.io/Getting StartedInstallationDependenciesFunctionalityOverviewInput Data FormatUsage ExampleOutputFundingLicensingGetting StartedThese are the instructions on how to get a copy of this project up
and use it for data analysis.InstallationThe software runs in Python >= 3.8To install DECNEO as a package:$ pip install decneoAlternatively, clone a local copy of this project to
install the package from the cloned directory:git clone https://github.com/sdomanskyi/decneo
python setup.py installDependenciesDECNEO is dependent on the following packages, that are installed/updated with installation of DECNEO:Matplotlib - plotting from PythonNetworkX - used in network enrichment analysisPandas and tables - for data storage and analysisNumPy - for processing datasklearn - we use clustering algorithms and metricsadjustText - optimization of text labels locations in plotsFunctionalityOverviewThe main implementation of DECNEO includes workflow for fast and efficient calculation of
single cell gene expression distance (e.g. correlation) followed by the bootstrap technique
to account for variation and noise in the input data. The results are summarized in
a form of a optimized dendrogram, heatmap and information panels. Analysis of combination
of measurements panels allows to identify main and secondary groups of genes that are coexpressed
in the cell type of interest.Input Data FormatExpression data fortwo differentspecies for comparison is required.
For each of these species provide the input gene expression data is expected in one of the following formats:Spreadsheet of comma-separated valuescsvwhere rows are genes, columns are cells with gene expression counts, this should be accompanied by another dataframe with two columns with one specifying batches and the other specifying corresponding cells.
Alternatively, the first row of the dataframe should be'batch'and the second'cell'.Pandas DataFramewhereaxis 0is genes andaxis 1are cells.
If the are batched in the data then the index ofaxis 1should 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.For examples refer to documentation.Usage ExampleWe have made an example execution filedemo.pythat shows how to usedecneo.Download fileVoightChoroid4567RemappedData.h5(456.7 Mb)
fromhttps://doi.org/10.5281/zenodo.4419880This file contains normalized gene expression of 27504 genes of 7996 endothelial cells from
8 batches, and 5704 non-endothelial cells from 8 batches. Genes that are not expressed in
endothelial cells are removed from non-endothelial cells datasetSave the downloaded data file todemo/, or otherwise modify path indemoDataofdemo.py:See details of the scriptdemo.pyat:Example walkthrough of demo.py scriptTo execute the complete scriptdemo.pyrun:python demo.pyIf reading demo data gives error "unsupported pickle protocol: 5" make sure that python 3.8 is used and
latest version of pandas and tables is installed.OutputOutputs all resulting directories, files, and figures to directory specified as theworkingDirwhen creating an instance of classAnalysis.
It will also output an analysis report detailing all results and figures.For a detailed list, refer to the documentation.FundingThis research project is a part of R01GM122085 grant, funded by NIH/NIGMS.LicensingDECNEO is released under an MIT License. Please also consult the folder LICENSES distributed with DECNEO regarding Licensing information for use of external associated content.