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bioRxiv · 10.1101/2022.02.07.479293

iDESC: Identifying differential expression in single-cell RNA sequencing data with multiple subjects

Liu, Y.·Wang, N.·Adams, T. S.·Schupp, J. C.·Wu, W.·McDonough, J. E.·Chupp, G. L.·Kaminski, N.·Wang, Z.·Yan, X.
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Abstract

Single-cell RNA sequencing (scRNA-seq) enables assessment of transcriptome-wide changes at single-cell resolution. However, dominant subject effect in scRNA-seq datasets with multiple subjects severely confounds cell-type-specific differential expression (DE) analysis. We developed iDESC to separate subject effect from disease effect with consideration of dropouts to identify DE genes. iDESC was shown to have well-controlled type I error and high power compared to existing methods and obtained the best consistency between datasets and disease relevance in two scRNA-seq datasets from same disease, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

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Liu, Y., Wang, N., Adams, T. S., Schupp, J. C., Wu, W., McDonough, J. E., Chupp, G. L., Kaminski, N., Wang, Z., Yan, X.. 2022-02-10. iDESC: Identifying differential expression in single-cell RNA sequencing data with multiple subjects. https://doi.org/10.1101/2022.02.07.479293

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