Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data-Reference-Cited by-同舟云学术

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Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data

Published:2022-08-08 Issue:1 Volume:17 Page:
ISSN:2095-2228
Container-title:Frontiers of Computer Science
language:en
Short-container-title:Front. Comput. Sci.

Author:

Matsuda Momo,Futamura Yasunori,Ye Xiucai,Sakurai Tetsuya

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11704-022-1172-z.pdf

Reference34 articles.

1. Oshlack A, Robinson M D, Young M D. From RNA-seq reads to differential expression results. Genome Biology, 2010, 11(12): 220

2. Ye X, Zhang W, Sakurai T. Adaptive unsupervised feature learning for gene signature identification in non-small-cell lung cancer. IEEE Access, 2020, 8: 154354–154362

3. Ozsolak F, Milos P M. RNA sequencing: advances, challenges and opportunities. Nature Reviews Genetics, 2011, 12(2): 87–98

4. Wagner A, Regev A, Yosef N. Revealing the vectors of cellular identity with single-cell genomics. Nature Biotechnology, 2016, 34(11): 1145–1160

5. Kiselev V Y, Andrews T S, Hemberg M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nature Reviews Genetics, 2019, 20(5): 273–282

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1. Mining order-preserving submatrix based on charm for gene expression data;Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023);2023-10-17

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