Miscell: An efficient self-supervised learning approach for dissecting single-cell transcriptome

Shen, Hongru; Li, Yang; Feng, Mengyao; Shen, Xiaohui; Wu, Dan; Zhang, Chao; Yang, Yichen; Yang, Meng; Hu, Jiani; Liu, Jilei; Wang, Wei; Zhang, Qiang; Song, Fang; Yang, Jilong; Chen, Kexin; Li, Xiangchun

doi:10.1016/j.isci.2021.103200

Cited by 10 publications

(10 citation statements)

References 45 publications

(108 reference statements)

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“…However, our benchmarking of SSL methods revealed the sensitivity to the choice of pre-training strategy. While contrastive methods have shown efficacy in other domains 41,43,44 and specialized in smaller scales in SCG 17,18,[20][21][22][23][24][25][26] , our study found that standard contrastive approaches did not yield as promising results for diverse, large-scale SCG tasks. This result highlights the challenges of applying these methods as generalizable pretext tasks for single-cell data.…”

Section: A Tailored Pre-training Strategy Leads To High Zero-shot Per...mentioning

confidence: 66%

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Richter,

Bahrami,

Xia

et al. 2024

Preprint

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Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabeled datasets, already transforming computer vision and natural language processing. Similarly, in single-cell genomics (SCG), representation learning is well-recognized for offering insights into complex biological data, even more so by the advent of early foundation model approaches. However, despite these advancements, identifying scenarios in SCG where SSL outperforms traditional supervised or unsupervised learning methods remains a nuanced challenge. Furthermore, selecting the most effective pretext tasks within the SSL framework for SCG is a critical yet unresolved question. Here, we address this gap by adapting and benchmarking SSL techniques in SCG, including masked autoencoders with multiple masking strategies and contrastive learning approaches. Trained on over 20 million cells, this study rigorously examines multiple downstream tasks, including cell type prediction, gene expression reconstruction, cross-modality prediction, and data integration. Our empirical analyses underscore the nuanced role of SSL, namely in transfer learning scenarios leveraging auxiliary data or analyzing novel datasets. Masked autoencoders excel over contrastive methods in SCG, diverging from computer vision trends. Moreover, our findings reveal notable capabilities of SSL in zero-shot cell type prediction and offer insights into its potential benefits in cross-modality prediction and data integration. In summary, we study the application of SSL in SCG, minimizing model bias through simple, fully connected networks, and benchmark SSL's utility across key representation learning scenarios.

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Section: A Tailored Pre-training Strategy Leads To High Zero-shot Per...mentioning

confidence: 66%

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Richter,

Bahrami,

Xia

et al. 2024

Preprint

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“…We identified 50 distinctive cell clusters (Fig. 1B ) that belong to immune cells and non-immune cells by Miscell 13 (see Methods section). The immune cells were primarily divided into T cells ( CD3D, CD3E ), B cells ( MS4A1, CD79A ), natural killer cells ( FGFBP2, KLRD1 ), monocytes, and macrophages ( LYZ, CD68, CD14 ).…”

Section: Resultsmentioning

confidence: 99%

“…The gene expression matrix was normalized by log2 transformation and scaled each gene by subtracting its mean and dividing with standard deviation. We extracted feature representations of single-cell using Miscell 13 . The gene expression signatures of single cells were captured by deep neural network.…”

Section: Methodsmentioning

confidence: 99%

A single-cell analysis reveals tumor heterogeneity and immune environment of acral melanoma

et al. 2022

Self Cite

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Acral melanoma is a dismal subtype of melanoma occurring in glabrous acral skin, and has a higher incidence in East Asians. We perform single-cell RNA sequencing for 63,394 cells obtained from 5 acral and 3 cutaneous melanoma samples to investigate tumor heterogeneity and immune environment. We define 5 orthogonal functional cell clusters that are involved in TGF-beta signaling, Type I interferon, Wnt signaling, Cell cycle, and Cholesterol efflux signaling. Signatures of enriched TGF-beta, Type I interferon, and cholesterol efflux signaling are significantly associated with good prognosis of melanoma. Compared with cutaneous melanoma, acral melanoma samples have significantly severe immunosuppressive state including depletion of cytotoxic CD8+ T cells, enrichment of Treg cells, and exhausted CD8+ T cells. PD1 and TIM-3 have higher expression in the exhaustive CD8+ T cells of acral melanoma. Key findings are verified in two independent validation sets. This study contributes to our better understanding of acral melanoma.

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“…Accompanied by the development of single-cell-based [177] and spatial-based [178] technologies that have been applied in molecular studies, numerous DL models are becoming more popular for computationally intensive analysis. To deal with the complexity of large genomics data, unsupervised deep clustering tools have been built for population structure identification [179] or cell population subtype annotation [180] , [181] , [182] , [183] . In addition, to process the complex structure of multi-omics data, graph neural network (GNN) models are increasingly popular in dataset integration [184] , biomedical classification [185] , prognosis prediction [186] , and so on.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

Yang

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

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Miscell: An efficient self-supervised learning approach for dissecting single-cell transcriptome

Cited by 10 publications

References 45 publications

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

A single-cell analysis reveals tumor heterogeneity and immune environment of acral melanoma

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

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