A Streamlined scRNA-Seq Data Analysis Framework Based on Improved Sparse Subspace Clustering

A Streamlined scRNA-Seq Data Analysis Framework Based on Improved Sparse Subspace Clustering

One advantage of single-cell RNA sequencing is its ability in revealing cell heterogeneity by cell clustering. However, cell clustering based on single-cell RNA sequencing is challenging due to the high transcript amplification noise, sparsity and outlier cell populations. In this study, we propose...

詳細記述

書誌詳細
主要な著者: Jujuan Zhuang, Lingyu Cui, Tianqi Qu, Changjing Ren, Junlin Xu, Tianbao Li, Geng Tian, Jialiang Yang
フォーマット: 論文
言語:English
出版事項: IEEE 2021-01-01
シリーズ:IEEE Access
主題:
Linearized alternating direction method of multipliers
low-rank representation
single-cell RNA sequencing
sparse subspace clustering
オンライン･アクセス:https://ieeexplore.ieee.org/document/9316660/

インターネット

https://ieeexplore.ieee.org/document/9316660/

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