**CytoTRACE 2 version 1.1.0** is packed with significant performance enhancements. Here's what's new in this release:
Major Updates and Enhancements
- **Retrained CytoTRACE 2 Framework**
The CytoTRACE 2 model has been retrained, yielding additional performance gains in granular potency prediction and enhancing cross-platform robustness.
- **Expanded Ensemble Model**
The ensemble now comprises **19 models** instead of 17, improving the predictive power and stability of the framework.
- **Background Expression Matrix**
Introduced a background expression matrix generated during training for improved regularization.
- **Enhanced Data Representations**
Added Log2-adjusted representation of the input expression data to be used for prediction on top of ranked expression profiles, to capture detailed transcriptomic signals. *This changes the requirement for the input expression data to contain only raw or CPM/TPM normalized counts.*
- **Adaptive Nearest Neighbor Smoothing**
Modified the KNN smoothing step to employ an adaptive nearest neighbor smoothing strategy.
Codebase and Distribution Updates
- **Codebase Updates**
- Updated both R and Python package codebases to reflect all the above changes.
- Optimized for **time and memory efficiency**, ensuring faster computations and scalability.
- **Enhanced Python Package Distribution**
The Python version of CytoTRACE 2 is now available on [PyPI](https://pypi.org/project/cytotrace2-py/), making installation easier for Python users.
Documentation and Guides
- Updated Vignettes to align with the new model features and usage instructions.
- Refreshed README with new information, detailed explanations, and FAQ items tailored to the new framework.