| Title: | Genotype-free demultiplexing of pooled single-cell RNA-seq |
| Journal: | Genome Biology |
| Published: | 19 Dec 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31856883/ |
| DOI: | https://doi.org/10.1186/s13059-019-1852-7 |
| URL: | https://genomebiology.biomedcentral.com/track/pdf/10.1186/s13059-019-1852-7 |
| Citations: | 69 (32 in last 2 years) as of 8 Aug 2024 |
Publication 7639
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Abstract
A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit18 Authors
- Jun Xu
- Caitlin Falconer
- Quan Nguyen
- Joanna Crawford
- Brett D. McKinnon
- Sally Mortlock
- Anne Senabouth
- Stacey Andersen
- Han Sheng Chiu
- Longda Jiang
- Nathan J. Palpant
- Jian Yang
- Michael D. Mueller
- Alex W. Hewitt
- Alice Pébay
- Grant W. Montgomery
- Joseph E. Powell
- Lachlan J.M Coin
1 Application
| Application ID | Title |
|---|---|
| 12514 | The limits of predicting complex traits and diseases from genetic data |
Enabling scientific discoveries that improve human health