On the importance of data transformation for data integration in single-cell RNA sequencing analysis

Recent advances in single-cell RNA (scRNA) sequencing have opened a multitude of possibilities to study tissues down to the level of cellular populations. Subsequently, this enabled various scRNA studies that reported novel or previously undetected subpopulations and their functions by integrating multiple datasets. However, the heterogeneity in single-cell sequencing data makes it unfeasible to adequately integrate multiple datasets generated from different studies. This heterogeneity originates from various sources of noise due to technological limitations. Thus, particular procedures are required to adjust such effects prior to further integrative analysis. Over the last years, numerous single-cell data analysis workflows have been introduced, implementing various read-count transformation methods for de-noising and batch correction. A detailed review of recent single-cell studies shows while many analysis procedures employ various preprocessing steps, they often neglect the importance of a well-chosen and optimized data transformation. This fact is particularly alarming since these data transformations can alter data distribution and thus have a crucial impact on subsequent downstream cell clustering results. Therefore, this study investigates the effects of the various data transformation methods on three different public data scenarios and evaluates them with the most commonly used dimensionality reduction and clustering analysis. Additionally, we discuss its implications for the subsequent application of different deep neural network approaches, such as auto encoders and transfer learning. In summary, our benchmark analysis shows that a large portion of batch effects and noise can be mitigated by simple but well-chosen data transformation methods. We conclude that such optimized preprocessing is crucial and should be the baseline for all comparative single-cell sequencing studies, particularely for integrative analysis of multiple data sets. ### Competing Interest Statement The authors have declared no competing interest.