1. scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases.
- Author
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Hu, Hongru and Quon, Gerald
- Abstract
Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation. Multimodal single-cell analysis faces challenges due to high feature dimensionality and shallow sequencing depth. Here, authors present scPair for aligning cell states across modalities with implicit feature selection and enhancing data analysis tasks such as identifying key transcription factors in neural differentiation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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