TY - GEN
T1 - An Automatic Recommendation Method for Single-Cell DNA Variant Callers Based on Meta-Learning Framework
AU - Wang, Jinhui
AU - Zhao, Xinyi
AU - Wang, Jiayin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The rapid expansion of single-cell sequencing-based research has motivated a proliferation of variant callers on the sequencing data. Due to the differences on calling strategies, these callers often exhibit varying performance when applied across heterogeneous sequencing samples. Selecting a suitable caller that fits for the data on-hand becomes an overwhelming task for researchers in this field. Thus, in this study, an automatic recommendation method for single-cell DNA (scDNA) variant callers is proposed. This recommender is designed on meta-learning framework. It explores the underlying associations between scDNA data features and the optimal variant caller on specific performance metric. The recommender is trained by benchmark sequencing datasets, and base on this, recommend appropriate caller for new sequencing data. A series of experiments on different datasets and various configurations have been conducted to validate the proposed method. The results demonstrate that the average performance of this recommendation method outperforms fixed and random strategies.
AB - The rapid expansion of single-cell sequencing-based research has motivated a proliferation of variant callers on the sequencing data. Due to the differences on calling strategies, these callers often exhibit varying performance when applied across heterogeneous sequencing samples. Selecting a suitable caller that fits for the data on-hand becomes an overwhelming task for researchers in this field. Thus, in this study, an automatic recommendation method for single-cell DNA (scDNA) variant callers is proposed. This recommender is designed on meta-learning framework. It explores the underlying associations between scDNA data features and the optimal variant caller on specific performance metric. The recommender is trained by benchmark sequencing datasets, and base on this, recommend appropriate caller for new sequencing data. A series of experiments on different datasets and various configurations have been conducted to validate the proposed method. The results demonstrate that the average performance of this recommendation method outperforms fixed and random strategies.
KW - Sequencing data analysis
KW - meta-learning
KW - single-cell DNA sequencing
KW - software recommendation
UR - https://www.scopus.com/pages/publications/85200504544
U2 - 10.1007/978-981-97-5131-0_23
DO - 10.1007/978-981-97-5131-0_23
M3 - 会议稿件
AN - SCOPUS:85200504544
SN - 9789819751303
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 269
EP - 280
BT - Bioinformatics Research and Applications - 20th International Symposium, ISBRA 2024, Proceedings
A2 - Peng, Wei
A2 - Cai, Zhipeng
A2 - Skums, Pavel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Symposium on Bioinformatics Research and Applications, ISBRA 2024
Y2 - 19 July 2024 through 21 July 2024
ER -