Single-cell multi-omics integration
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Single-cell multi-omics integration describes a suite of computational methods used to harmonize information from multiple "omes" to jointly analyze biological phenomena. This approach allows researchers to discover intricate relationships between different chemical-physical modalities by drawing associations across various molecular layers simultaneously. Multi-omics integration approaches can be categorized into four broad categories: Early integration, intermediate integration, late integration, and mixed integration methods. The selection of an optimal approach depends on factors such as whether the single-cell dataset is matched or unmatched, with different implementations tailored to suit the specific use case. Multi-omics integration can enhance experimental robustness by providing independent sources of evidence to address hypotheses, leveraging modality-specific strengths to compensate for another's weaknesses through imputation, and offering cell-type clustering and visualizations that are more aligned with reality.
Background
Methodology
Early Integration
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Intermediate Integration
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Mixed Integration
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Late Integration
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Dimensionality Reduction
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Considerations of Data Integration
Noise
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Dataset Compatibility
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Dimensionality
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Oversimplification of Modality Mapping
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Interpretability and Validation
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Matched and Unmatched Data
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