scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets.
- Publisher:
- Proceedings of the National Academy of Sciences
- Publication Type:
- Journal Article
- Citation:
- Proc Natl Acad Sci U S A, 2019, 116, (20), pp. 9775-9784
- Issue Date:
- 2019-05-14
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| pnas.1820006116.pdf | Published version | 1.72 MB |
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Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.
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