A User Study of Two Downstream Single-Cell Data Analysis Methods: Clustering and Trajectory Inference

Recent advancements in deep learning have significantly improved the analysis of single-cell data, including clustering and trajectory inference. Multiple methods have been proposed for these downstream tasks. However, researchers often rely on only a few metrics to assess these methods, disregarding whether users can effectively derive useful information from their outputs. To address this gap, we conducted a user study comparing various downstream single-cell analysis methods, including a post-questionnaire to indicate user preferences, user-friendliness, and customization capabilities. We also provided a brief analysis of the methods examined in this study. We conclude that user preferences vary across different aspects.