scLASER

Longitudinal single-cell clinical studies enable tracking within-individual cellular dynamics, but methods for modeling temporal phenotypic changes and estimating power remain limited. We present scLASER, a framework detecting time-dependent cellular neighborhood dynamics and simulating longitudinal single-cell datasets for power estimation. Across benchmarks, scLASER shows superior sensitivity, particularly for non-linear temporal patterns. Applications to inflammatory bowel disease reveal treatment-responsive NOTCH3+ stromal trajectories, while analysis of COVID-19 data identifies distinct axes of T cell activity over disease progression. scLASER enables robust longitudinal single-cell analysis and optimization of study design.

What does scLASER do?

• Simulate multi-timepoint longitudinal single-cell datasets with distinct dynamic patterns for clinical outcome (e.g., treatment response).
• Detect time-dependent cellular dynamics (linear and nonlinear) associated with treatment response.
• Generate a per-cell association score quantifying each cell's contribution to time x response.
• Validate cell-type classification performance for predicting time x response interactions.

Installation

To install the latest development version directly from GitHub:

devtools::install_github("fanzhanglab/scLASER")

Dependencies

- R (>= 4.1.0)
- methods
- stats
- utils
- Matrix
- nlme
- lme4
- pbapply
- purrr
- caret
- uwot
- Seurat
- harmony
- broom.mixed
- foreach
- doParallel
- moments

Tutorials

• scLASER longitudinal data analytical tutorial
  Detecting cellular neighborhood dynamics for time-dependent clinical outcome changes (e.g., treatment response, disease progression).

• scLASER longitudinal data simulation tutorial
  Simulating longitudinal single-cell datasets with user-defined clinical outcome trajectories, number of timepoints, demographic structures, number of individuals, number of cells, and technical variability.

Citations

Vanderlinden LA, Vargas J, Inamo J, Young J, Wang C, Zhang F. scLASER: A robust framework for simulating and detecting time-dependent single-cell dynamics in longitudinal studies. , In submission.

Help, Suggestion and Contribution

Using github issues section, if you have any question, comments, suggestions, or to report coding related issues of scLASER is highly encouraged than sending emails.

• Please check the GitHub issues for similar issues that has been reported and resolved. This helps the team to focus on adding new features and working on cool projects instead of resolving the same issues!
• Examples are required when filing a GitHub issue. In certain cases, please share your scLASER object and related codes to understand the issues.

Contact

Please contact [email protected] for further questions or potential collaborative opportunities!