Are dropout imputation methods for scRNA-seq effective for scATAC-seq data?
| العنوان: | Are dropout imputation methods for scRNA-seq effective for scATAC-seq data? |
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| المؤلفون: | Xiangxiang Zeng, Wei Zhang, Shu-Lin Wang, Jun-Feng Zhang, Yue Liu |
| المصدر: | Briefings in Bioinformatics. 23 |
| بيانات النشر: | Oxford University Press (OUP), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Source code, Sequence Analysis, RNA, Computer science, media_common.quotation_subject, Magic (programming), Inference, computer.software_genre, Deep sequencing, Identification (information), Exome Sequencing, Cluster Analysis, Data mining, Imputation (statistics), Single-Cell Analysis, Cluster analysis, Molecular Biology, computer, Software, Dropout (neural networks), Information Systems, media_common |
| الوصف: | The tremendous progress of single-cell sequencing technology has given researchers the opportunity to study cell development and differentiation processes at single-cell resolution. Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) was proposed for genome-wide analysis of chromatin accessibility. Due to technical limitations or other reasons, dropout events are almost a common occurrence for extremely sparse single-cell ATAC-seq data, leading to confusion in downstream analysis (such as clustering). Although considerable progress has been made in the estimation of scRNA-seq data, there is currently no specific method for the inference of dropout events in single-cell ATAC-seq data. In this paper, we select several state-of-the-art scRNA-seq imputation methods (including MAGIC, SAVER, scImpute, deepImpute, PRIME, bayNorm and knn-smoothing) in recent years to infer dropout peaks in scATAC-seq data, and perform a systematic evaluation of these methods through several downstream analyses. Specifically, we benchmarked these methods in terms of correlation with meta-cell, clustering, subpopulations distance analysis, imputation performance for corruption datasets, identification of TF motifs and computation time. The experimental results indicated that most of the imputed peaks increased the correlation with the reference meta-cell, while the performance of different methods on different datasets varied greatly in different downstream analyses, thus should be used with caution. In general, MAGIC performed better than the other methods most consistently across all assessments. Our source code is freely available at https://github.com/yueyueliu/scATAC-masterTest. |
| تدمد: | 1477-4054 1467-5463 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::106b10c87966124ebdae66e824ca7009Test https://doi.org/10.1093/bib/bbab442Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....106b10c87966124ebdae66e824ca7009 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14774054 14675463 |
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