Influence of single-cell RNA sequencing data integration on the performance of differential gene expression analysis.

ABSTRACT: Large-scale comprehensive single-cell experiments are often resource-intensive and require the involvement of many laboratories and/or taking measurements at various times. This inevitably leads to batch effects, and systematic variations in the data that might occur due to different technology platforms, reagent lots, or handling personnel. Such technical differences confound biological variations of interest and need to be corrected during the data integration process. Data integration is a challenging task due to the overlapping of biological and technical factors, which makes it difficult to distinguish their individual contribution to the overall observed effect. Moreover, the choice of integration method may impact the downstream analyses, including searching for differentially expre

SUBMITTER: Kujawa T 

PROVIDER: S-EPMC9663917 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature