Releases · digitalcytometry/cytotrace2

CytoTRACE 2 version 1.1.0 is packed with significant performance enhancements. Here's what's new in this release:

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 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, making installation easier for Python users.

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.

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