A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915629711228928 |
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| author | Anaissi, Ali Liu, Deshao Jia, Yuanzhe Huang, Weidong Alyassine, Widad Akram, Junaid |
| author_facet | Anaissi, Ali Liu, Deshao Jia, Yuanzhe Huang, Weidong Alyassine, Widad Akram, Junaid |
| contents | Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated datasets, SCR-MF achieves robust and interpretable performance comparable to or exceeding existing imputation methods in most cases, while preserving biological fidelity and transparency. Runtime analysis demonstrates that SCR-MF provides a competitive balance between accuracy and computational efficiency, making it suitable for mid-scale single-cell datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests Anaissi, Ali Liu, Deshao Jia, Yuanzhe Huang, Weidong Alyassine, Widad Akram, Junaid Machine Learning Genomics Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals. We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest. Across public and simulated datasets, SCR-MF achieves robust and interpretable performance comparable to or exceeding existing imputation methods in most cases, while preserving biological fidelity and transparency. Runtime analysis demonstrates that SCR-MF provides a competitive balance between accuracy and computational efficiency, making it suitable for mid-scale single-cell datasets. |
| title | A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests |
| topic | Machine Learning Genomics |
| url | https://arxiv.org/abs/2511.16923 |