Use of “default” parameter settings when analyzing single cell RNA sequencing data using Seurat: a biologist’s perspective

Abstract

Aim: Analysis of large datasets has become integral to biological studies due to the advent of high throughput technologies such as next generation sequencing. Techniques for analyzing these large datasets are normally developed by bioinformaticists and statisticians, with input from biologists. Frequently, the end-user does not have the training or knowledge to make informed decisions on input parameter settings required to implement the analyses pipelines. Instead, the end-user relies on “default” settings present within the software packages, consultations with in-house bioinformaticists, or on methods described in previous publications. The aim of this study was to explore the effects of altering default parameters on the cell clustering solutions generated by a common pipeline implemented in the Seurat R package that is used to cluster cells based on single cell RNA sequencing (scRNAseq) data. Methods: We systematically assessed the effect of altering input parameters by performing iterative analyses on a single scRNAseq dataset. We compared the clustering solutions using the different input parameters to determine which parameters have a large effect on cell clustering solutions. Results: We used a range of input parameters for many, but not all, of the input parameters required by the Seurat R pipeline. We found that some input parameters had a very small effect on the clustering solution, while other parameters had a much larger effect. Conclusion: We conclude that, when implementing the Seurat R package, the “default” parameters should be used with caution. We identified specific parameters that have a significant effect on clustering solutions.