HelPredictor

HelPredictor predicts human embryo lineage allocation from single-cell transcriptome data to model lineage-specific patterns and developmental trajectories in preimplantation embryos.

Key Features:

• Integration of Feature Selection Methods: Applies Principal Components Analysis (PCA), the F-score algorithm, and Squared Coefficient of Variation for feature selection from single-cell transcriptome data.
• Utilization of Classical Machine Learning Classifiers: Combines outputs from four classical machine learning classifiers across different feature-selection combinations to generate lineage predictions.
• High Predictive Accuracy: Reports 94.9% cross-validation accuracy and 90.9% independent test accuracy for embryonic lineage classification.
• Efficient Classification with Reduced Factors: Classifies embryonic lineages using a reduced set of predicted factors to limit feature dimensionality.
• Exploration of Embryonic Heterogeneity: Identifies and clusters candidate lineage-specific genes to explore transitions and heterogeneity in preimplantation embryos.

Scientific Applications:

• Lineage allocation and trajectory inference: Infer lineage allocation and developmental trajectories in human preimplantation embryos from single-cell transcriptome data.
• Biomarker identification: Identify candidate lineage-specific and stage-specific biomarkers for embryonic development.
• Heterogeneity analysis: Characterize cellular heterogeneity and transitional states among embryonic cell clusters.
• Molecular event interpretation: Provide insights into molecular events associated with embryonic fate decisions.

Methodology:

Performs feature selection using PCA, F-score algorithm, and Squared Coefficient of Variation, inputs selected features into four classical machine learning classifiers, and evaluates performance via cross-validation and independent testing (reported accuracies: 94.9% cross-validation, 90.9% independent test).