Single Cell Informatics#

A user-friendly toolkit for QC, counting, clustering and plotting of single-cell (epi)genomics data.

sincei is described in our preprint: Bhardwaj V. , Mourragui, S. (2024) User-friendly exploration of epigenomic data in single cells using sincei.

Installation#

sincei is a command line toolkit based on python3. The stable version of sincei can be installed using conda , while the development versions can be installed from github via pip.

Installation via bioconda#

Create a new conda environment and install sincei using:

conda create -n sincei -c bioconda -c conda-forge sincei

Mac users with Arm architecture (M1-3 macbooks and beyond) should explicitly specify the osx-64 version to allow dependencies to install properly.

conda create -n sincei --subdir 'osx-64' -c bioconda -c conda-forge sincei

Note: The dependency mctorch-lib required for scClusterCells is currently unavailable via conda, therefore, to use scClusterCells, we recommend installing it separately via pip.

# install mctorch-lib
(sincei): pip install mctorch-lib
(sincei): scClusterCells --help

Installation via github#

Create a new conda environment and install sincei in it using pip from GitHub:

conda create -n sincei python=3.10
conda activate sincei
(sincei): pip install git+https://github.com/bhardwaj-lab/sincei.git@master#egg=sincei

Getting Help#

For questions related to usage, or suggesting changes/enhancements please use our GitHub discussion board . To report bugs, please create an issue on our GitHub repository

Please Note that sincei is under active development. We expect significant changes/updates as we move towards our first major release (1.0).

Command line tools available in sincei#

Tools for a typical single-cell analysis workflow (WIP: work in progress/not available yet)

tool	description
scFilterBarcodes	Identify and filter cell barcodes from BAM file (for droplet-based single-cell seq).
scFilterStats	Produce per-cell statistics after filtering reads by user-defined criteria.
scJSD	Compare the cumulative read coverages of cells on regions using the Jensen-Shannon distance.
scCountReads	Counts reads for each barcode on genomic bins or user-defined features.
scCountQC	Perform quality control and filter the output of scCountReads.
scCombineCounts	Concatenate/merge the counts from different samples/batches or different modalities.
scClusterCells	Perform dimensionality reduction and clustering on the output of scCountReads.
scBulkCoverage	Get pseudo-bulk coverage per group using a user-supplied cell->group mapping (output of scClusterCells).
scFindMarkers	[WIP] Find marker genes per group, given the output of scCountReads and a user-defined group.
scFeaturePlot	[WIP] Plot the counts for a given feature on a UMAP or on a (IGV-style) genomic-track.

Single Cell Informatics

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