scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication

Lin, Y.; Ghazanfar, S.; Wang, K.; Gagnon-Bartsch, J. A.; Lo, K. K.; Su, X.; Han, Z.-G.; Ormerod, J. T.; Speed, T. P.; Yang, P.; Yang, J. Y. H.

doi:10.1101/393280

bioRxiv · 10.1101/393280

scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication

Lin, Y.·Ghazanfar, S.·Wang, K.·Gagnon-Bartsch, J. A.·Lo, K. K.·Su, X.·Han, Z.-G.·Ormerod, J. T.·Speed, T. P.·Yang, P.·Yang, J. Y. H.

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Abstract

Concerted examination of multiple collections of single cell RNA-Seq (scRNA-Seq) data promises further biological insights that cannot be uncovered with individual datasets. However, such integrative analyses are challenging and require sophisticated methodologies. To enable effective interrogation of multiple scRNA-Seq datasets, we have developed a novel algorithm, named scMerge, that removes unwanted variation by combining stably expressed genes and utilizing pseudo-replicates across datasets. Analysis of large collections of publicly available datasets demonstrates that scMerge performs well in multiple scenarios and enhances biological discovery, including inferring cell developmental trajectories.

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Lin, Y., Ghazanfar, S., Wang, K., Gagnon-Bartsch, J. A., Lo, K. K., Su, X., Han, Z.-G., Ormerod, J. T., Speed, T. P., Yang, P., Yang, J. Y. H.. 2018-08-16. scMerge: Integration of multiple single-cell transcriptomics datasets leveraging stable expression and pseudo-replication. https://doi.org/10.1101/393280

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