Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for single-cell RNA sequencing
Creators
Description
Ensemblex is an accuracy-weighted ensemble framework for genetic demultiplexing of pooled single-cell RNA seqeuncing (scRNAseq) data. Ensemblex can be used to demultiplex pools with or without prior genotype information. When demultiplexing with prior genotype information, Ensemblex leverages the sample assignments of four individual, constituent genetic demultiplexing tools:
- Demuxalot (Rogozhnikov et al. )
- Demuxlet (Kang et al. )
- Souporcell (Heaton et al. )
- Vireo-GT (Huang et al. )
When demultiplexing without prior genotype information, Ensemblex leverages the sample assignments of four individual, constituent genetic demultiplexing tools:
- Demuxalot (Rogozhnikov et al. )
- Freemuxlet (Kang et al. )
- Souporcell (Heaton et al. )
- Vireo (Huang et al. )
Upon demultiplexing pools with each of the four constituent genetic demultiplexing tools, Ensemblex processes the output files in a three-step pipeline to identify the most probable sample label for each cell based on the predictions of the constituent tools:
Step 1: Probabilistic-weighted ensemble
Step 2: Graph-based doublet detection
Step 3: Ensemble-independent doublet detection
Files
ensemblex.pip.zip
Files
(3.2 GB)
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Additional details
Funding
- Michael J. Fox Foundation
- Canadian Institutes of Health Research
Software
- Repository URL
- https://github.com/neurobioinfo/ensemblex
- Programming language
- R, Shell