Flexible Experimental Designs for Valid Single-cell RNA-sequencing Experiments Allowing Batch Effects Correction
Publication in refereed journal


引用次數
Scopus ( 26/11/2020)
替代計量分析
.

其它資訊
摘要Despite their widespread applications, single-cell RNA-sequencing (scRNA-seq) experiments are still plagued by batch effects and dropout events. Although the completely randomized experimental design has frequently been advocated to control for batch effects, it is rarely implemented in real applications due to time and budget constraints. Here, we mathematically prove that under two more flexible and realistic experimental designs—the reference panel and the chain-type designs—true biological variability can also be separated from batch effects. We develop Batch effects correction with Unknown Subtypes for scRNA-seq data (BUSseq), which is an interpretable Bayesian hierarchical model that closely follows the data-generating mechanism of scRNA-seq experiments. BUSseq can simultaneously correct batch effects, cluster cell types, impute missing data caused by dropout events, and detect differentially expressed genes without requiring a preliminary normalization step. We demonstrate that BUSseq outperforms existing methods with simulated and real data.
著者SONG Fangda, CHAN Ga Ming Angus, Wei Yingying
期刊名稱Nature Communications
出版年份2020
月份7
卷號11
期次1
出版社Nature Research
文章號碼3274
國際標準期刊號2041-1723
語言美式英語

上次更新時間 2020-27-11 於 00:01