Flexible Experimental Designs for Valid Single-cell RNA-sequencing Experiments Allowing Batch Effects Correction

Abstract

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.

SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data

“…In this section, we benchmark SCRIBE with existing imputation methods including BUSseq [18], Drimpute [23], scImpute [21], SAVER [24] and MAGIC [22]. To generate realistic benchmarking datasets, we perform down-sampling experiments [24] on Usoskin dataset.…”

“…Next, we benchmark SCRIBE with existing scRNA-seq batch effects correction methods including BUSseq [18], MNN [15], Seurat [14] and ZINB-WaVE [16]. The number of cell types is input to BUSseq with all other parameters set to default.…”

“…However, the existence of batch effects makes data integration difficult since they are often associated with an outcome of interest [3]. Since the earliest observation of batch effects in microarray experiments [4], an explosion of batch effects correction methods for both bulk RNA-seq data [5][6][7][8][9][10][11][12][13] and scRNA-seq data [14][15][16][17][18][19] have been developed over the past two decades. Moreover, the inflation of zero caused by dropout events [20] renders more noise to scRNA-seq data than bulk RNA-seq data.…”

“…Evidence has shown that batch effects account for a substantial percentage of dropout variability at the cell level [1]. Many imputation methods have been proposed in recent years to recover dropouts [18,[21][22][23][24] in scRNA-seq data. However, among these methods, only BUSseq [18] takes both batch effects and dropout events into consideration.…”