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• W2565353914 abstract "<em>Background</em>: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. <em>Results</em>: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. <em>Conclusions</em>: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data." @default.
• W2565353914 created "2017-01-06" @default.
• W2565353914 creator A5014184116 @default.
• W2565353914 creator A5081242795 @default.
• W2565353914 date "2016-12-01" @default.
• W2565353914 modified "2023-09-25" @default.
• W2565353914 title "Differential expression analyses for single-cell RNA-Seq: old questions on new data" @default.
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• W2565353914 doi "https://doi.org/10.1007/s40484-016-0089-7" @default.
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