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DLscID 🔬

Deep Learning single-cell Identification and Annotation
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This is the home of the pipeline, DLscID. Its long-term goals: to accurately identify and annotate cell types like no pipeline before!

Overview

Welcome to DLscID's documentation! This guide is the main source of documentation for users that are getting started with the Deep Learning single-cell Identification.

The ./DLscID pipeline is composed several inter-related sub commands to setup and run the pipeline across different systems. Each of the available sub commands perform different functions:

  • DLscID run: Run the DLscID pipeline with your input files.
  • DLscID unlock: Unlocks a previous runs output directory.
  • DLscID cache: Cache remote resources locally, coming soon!

DLscID is a comprehensive tool to identify and annotate cell types in single-cell RNA-seq data. It relies on technologies like Singularity1 to maintain the highest-level of reproducibility. The pipeline consists of a series of data processing and quality-control steps orchestrated by Snakemake2, a flexible and scalable workflow management system, to submit jobs to a cluster.

The pipeline is compatible with data generated from Illumina short-read sequencing technologies. As input, it accepts a set of FastQ files and can be run locally on a compute instance or on-premise using a cluster. A user can define the method or mode of execution. The pipeline can submit jobs to a cluster using a job scheduler like SLURM (more coming soon!). A hybrid approach ensures the pipeline is accessible to all users.

Before getting started, we highly recommend reading through the usage section of each available sub command.

For more information about issues or trouble-shooting a problem, please checkout our FAQ prior to opening an issue on Github.

Contribute

This site is a living document, created for and by members like you. DLscID is maintained by the members of NCBR and is improved by continous feedback! We encourage you to contribute new content and make improvements to existing content via pull request to our GitHub repository .

References

1. Kurtzer GM, Sochat V, Bauer MW (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE 12(5): e0177459.
2. Koster, J. and S. Rahmann (2018). "Snakemake-a scalable bioinformatics workflow engine." Bioinformatics 34(20): 3600.

Last update: 2022-10-18
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