Fig. 1: Research Framework and Overview of Machine Learning Methods.
From: Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer
a Pitfalls and risks in manual gating are schematically presented, highlighting the major pitfalls and risks associated with manual gating, emphasising the hand-drawn nature of the methodology that introduces bias and undermines reproducibility. Unicode emojis for warning and hand symbols are included83. b Proposed research framework for Machine Learning (ML)-assisted decoding of transcriptional dynamics. This schematic outlines the comprehensive workflow employed to unravel transcriptional dynamics of Foxp3 within a functional system. It covers the experimental design, generation of independent training and test datasets, training of ML models, performance evaluation, and data-driven identification of group-specific feature cells through model behaviour analysis. The protein structure is adapted from82. c Implementation of the research framework as TockyMachineLearning, a novel machine learning suite designed for this study. Data preprocessing, performed by TockyPrep, normalises and transforms flow cytometric Timer data into standardised Timer Angle and Timer Intensity data. This pre-processed data then feeds into the TockyMachineLearning toolkit. Within this toolkit, TockyKmeansRF combines k-means clustering with Random Forest (RF) analysis, utilising the mean decrease Gini index to identify feature cells. TockyConvNet transforms Timer Angle and Intensity data into 2D grayscale images representing cell density. These images are batch-processed by ConvNet, with model behaviours monitored using Grad-CAM to enable identification of feature cells at the single-cell level.
