Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• Single-cell gene expression profiling enables the study of transcriptional dynamics in complex biological processes like cell differentiation.
• Pseudotime analysis computationally infers developmental progression from static single-cell data when time-series experiments are infeasible.
• Existing pseudotime methods often provide point estimates, failing to account for inherent biological variability and uncertainty in cell ordering.

Purpose of the Study:

• To develop and apply probabilistic modeling techniques for quantifying uncertainty in pseudotime inference.
• To integrate pseudotime uncertainty into differential gene expression analysis.
• To evaluate the impact of pseudotime uncertainty on the reliability of biological conclusions.

Main Methods:

• Utilized probabilistic modeling to estimate pseudotime and its associated uncertainty for individual cells.
• Developed methods to propagate pseudotime uncertainty into differential expression analyses.
• Compared results from probabilistic approaches with those relying on point estimates of pseudotime.

Main Results:

• Demonstrated that point estimates of pseudotime can lead to inflated false discovery rates in differential expression.
• Showcased that probabilistic methods provide more robust estimates and a clearer understanding of temporal resolution.
• Quantified the implications of pseudotime uncertainty for interpreting dynamic biological processes.

Conclusions:

• Probabilistic modeling of pseudotime uncertainty is crucial for accurate analysis of single-cell temporal data.
• Ignoring pseudotime uncertainty can compromise the validity of findings in gene expression studies.
• This approach enhances the reliability and interpretability of single-cell trajectory inference.