  Recent advancements in single-cell RNA-sequencing (scRNA-seq) have enhanced
our understanding of cell heterogeneity at a high resolution. With the ability
to sequence over 10,000 cells per hour, researchers can collect large scRNA-seq
datasets for different participants, offering an opportunity to study the
temporal progression of individual-level single-cell data. However, the
presence of excessive zeros, a common issue in scRNA-seq, significantly impacts
regression/association analysis, potentially leading to biased estimates in
downstream analysis. Addressing these challenges, we introduce the Zero
Inflated Smoothing Spline (ZISS) method, specifically designed to model
single-cell temporal data. The ZISS method encompasses two components for
modeling gene expression patterns over time and handling excessive zeros. Our
approach employs the smoothing spline ANOVA model, providing robust estimates
of mean functions and zero probabilities for irregularly observed single-cell
temporal data compared to existing methods in our simulation studies and real
data analysis.
