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SIMLR: A Tool For Large-Scale Single-Cell Analysis By Multi-Kernel Learning [article]

Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou
2017 bioRxiv   pre-print
Motivation: We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell  ...  valuable insights by making the data more interpretable via better a visualization.  ...  large-scale extension (see Supplementary Material for details).  ... 
doi:10.1101/118901 fatcat:l4momx23m5axhkrkhxsoyq6d4i

SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning

Bo Wang, Daniele Ramazzotti, Luca De Sano, Junjie Zhu, Emma Pierson, Serafim Batzoglou
2018 Proteomics  
We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed  ...  valuable insights by making the data more interpretable via better a visualization.  ...  SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning Supplementary Information Bo Wang * Daniele Ramazzotti * Luca De Sano * Junjie Zhu Emma Pierson Serafim Batzoglou  ... 
doi:10.1002/pmic.201700232 pmid:29265724 fatcat:duktczfimvfqbaaovbjofb5x2m

A Hybrid Deep Clustering Approach for Robust Cell Type Profiling Using Single-cell RNA-seq Data: Supplementary Figures and Tables [article]

Suhas Srinivasan, Nathan T Johnson, Dmitry Korkin
2019 bioRxiv   pre-print
Here, we develop a new hybrid approach, Deep Unsupervised Single-cell Clustering (DUSC), that integrates feature generation based on a deep learning architecture with a model-based clustering algorithm  ...  Single-cell RNA sequencing (scRNA-seq) is a recent technology that enables fine-grained discovery of cellular subtypes and specific cell states.  ...  Eibe Frank for helpful information regarding the functionality of the Weka data mining tool.  ... 
doi:10.1101/511626 fatcat:fp2vlwr63jdsdfv3jdfjqitequ

Spectrum: fast density-aware spectral clustering for single and multi-omic data

2019 Bioinformatics  
Cluster analysis is also increasingly applied on single-omic data, for example, in single cell RNA-seq analysis for clustering the transcriptomes of individual cells.  ...  Our motivation was therefore to develop a flexible and effective spectral clustering tool for both single and multi-omic data.  ...  Acknowledgements This study was supported by funding from the UK Medical Research Council (MRC) (grant number G0800648). Funding No funding source to declare. Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/btz704 pmid:31501851 pmcid:PMC7703791 fatcat:dokzy3opvrh6ndgdegcjowvjuu

Spectrum: Fast density-aware spectral clustering for single and multi-omic data [article]

Christopher Robert John, David Watson, Michael Barnes, Costantino Pitzalis, Myles Lewis
2019 biorxiv/medrxiv   pre-print
We present Spectrum, a fast spectral clustering method for single and multi-omic expression data. Spectrum is flexible and performs well on single-cell RNA-seq data.  ...  The method uses a new density-aware kernel that adapts to data scale and density.  ...  Acknowledgements This study was supported by funding from the UK Medical Research Council (MRC) (grant number G0800648). Funding No funding source to declare. Conflict of Interest: none declared.  ... 
doi:10.1101/636639 fatcat:pzatf5glhvckzkkeyofntl2oiy

Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis

Mayssa Soussia, Islem Rekik
2018 Frontiers in Neuroinformatics  
single-cell interpretation via multikernel learning (SIMLR) (Wang et al., 2017) .  ...  Our choice for leveraging this algorithm is motivated by: (1) SIMLR can learn a similarity matrix from high-order networks by combining multiple kernels which provides a better fit to the inherent statistical  ...  This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).  ... 
doi:10.3389/fninf.2018.00070 pmid:30459585 pmcid:PMC6232924 fatcat:xg5ggeg4mffs5et2p2ncystnay

Learning association for single-cell transcriptomics by integrating profiling of gene expression and alternative polyadenylation [article]

Guoli Ji, Xiaohui Wu, Wujing Xuan, Yibo Zhuang, Lishan Ye, Sheng Zhu, Wenbin Ye, Xi Wang
2021 bioRxiv   pre-print
We proposed a toolkit called scLAPA for learning association for single-cell transcriptomics by combing single-cell profiling of gene expression and alternative polyadenylation derived from the same scRNA-seq  ...  As a comprehensive toolkit, scLAPA provides a unique strategy to learn cell-cell associations, improve cell type clustering and discover novel cell types by augmentation of gene expression profiles with  ...  SIMLR (Single-cell Interpretation via Multikernel Learning) adapts k-means by simultaneously training a similarity measure based on multiple kernel learning [4] .  ... 
doi:10.1101/2021.01.04.425335 fatcat:jgmf5zccqre4pjqry4hzqsljse

Interpretable, similarity-driven multi-view embeddings from high-dimensional biomedical data [article]

Brian B. Avants, Nicholas J. Tustison, James R. Stone
2021 arXiv   pre-print
We demonstrate that SiMLR outperforms closely related methods on supervised learning problems in simulation data, a multi-omics cancer survival prediction dataset and multiple modality neuroimaging datasets  ...  Similarity-driven multi-view linear reconstruction (SiMLR) is an algorithm that exploits inter-modality relationships to transform large scientific datasets into smaller, more well-powered and interpretable  ...  The multi-omic cancer survival prediction study uses SiMLR as a tool for supervised feature learning. The learned embeddings are linked with Cox-hazard regression to predict survival in test data.  ... 
arXiv:2006.06545v3 fatcat:xkxyd4orwjezfkacjfdezmh54u

Clustering with feature selection using alternating minimization, Application to computational biology [article]

Cyprien Gilet, Marie Deprez, Jean-Baptiste Caillau, Michel Barlaud
2019 arXiv   pre-print
The complexity of K-sparse is linear in the number of samples (cells), so that the method scales up to large datasets.  ...  Experiments on Single Cell RNA sequencing datasets show that our method significantly improves the results of PCA k-means, spectral clustering, SIMLR, and Sparcl methods, and achieves a relevant selection  ...  This figure shows the nice small ball-shaped clusters computed by k-sparse and SIMLR methods. ; Mixon et al. (2017); Peng and Wei ), SIMLR (Single-cell Interpretation via Multikernel Learning) (Wang  ... 
arXiv:1711.02974v4 fatcat:5ztoqrgug5dxrmqhojumowl5am

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement [article]

Zhenlan Liang, Min Li, Ruiqing Zheng, Yu Tian, Xuhua Yan, Jin Chen, Fangxiang Wu, Jianxin Wang
2020 bioRxiv   pre-print
In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE.  ...  Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies.  ...  clustering algorithm designed for large-scale single cell data, and it exploits an 211 approximate nearest neighbour search technique to reduce the time complexity of analyzing 212 large-scale  ... 
doi:10.1101/2020.04.08.028779 fatcat:zj7du2nkp5arfah2jfkewj3jru

AAE-SC: A scRNA-seq Clustering Framework based on Adversarial Autoencoder

Yulun Wu, Yanming Guo, Yandong Xiao, Songyang Lao
2020 IEEE Access  
In addition, SIMLR can process the large-scale datasets with heavy noise. MPSSC [16] innovatively used L1 penalty to characterize the sparsity of data with multi-kernel spectral clustering.  ...  Uses PCA to extract the main data features, and uses k-means for clustering [49] [44] SIMLR Spectral clustering with multiple Gaussian kernel similarity measures [15] MPSSC Multi-kernel spectral  ...  His current research interests include deep learning, image processing, video analysis and human-computer interaction.  ... 
doi:10.1109/access.2020.3027481 fatcat:agl6cbzjdvborji5dr3spxrfn4

Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing [article]

Romain Lopez, Jeffrey Regier, Michael B Cole, Michael Jordan, Nir Yosef
2018 bioRxiv   pre-print
Here, we introduce Single-cell Variational Inference (scVI), a scalable framework for probabilistic representation and analysis of gene expression in single cells.  ...  the state-of-the-art tools in each task. scVI is publicly available, and can be readily used as a principled and inclusive solution for multiple tasks of single-cell RNA sequencing data analysis.  ...  SIMLR We used the large scale version of the Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) algorithm from Bioconductor with parameters recommended by authors (k=30, kk=200).  ... 
doi:10.1101/292037 fatcat:2io7p2rczzbfzmbimxo44oa4vm

Clustering Single-Cell RNA-seq Data with Regularized Gaussian Graphical Model

Zhenqiu Liu
2021 Genes  
Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations.  ...  RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ.  ...  Data Availability Statement: The datasets analyzed for this study can be found in the public available scRNA-seq package in R (http://bioconductor.org/packages/release/data/experiment/ html/scRNAseq.html  ... 
doi:10.3390/genes12020311 pmid:33671799 pmcid:PMC7927011 fatcat:6zdp7u6eu5gb7j7y4ygvitk2py

Entropy subspace separation-based clustering for noise reduction (ENCORE) of scRNA-seq data

Jia Song, Yao Liu, Xuebing Zhang, Qiuyue Wu, Juan Gao, Wei Wang, Jin Li, Yanling Song, Chaoyong Yang
2020 Nucleic Acids Research  
We named such strategy as 'entropy subspace' separation and designed a cell clustering algorithm called ENtropy subspace separation-based Clustering for nOise REduction (ENCORE) by integrating the 'entropy  ...  Single-cell RNA sequencing enables us to characterize the cellular heterogeneity in single cell resolution with the help of cell type identification algorithms.  ...  ACKNOWLEDGEMENTS We thank every member of Dr Yang and Dr Li's lab for their helpful discussion and suggestions.  ... 
doi:10.1093/nar/gkaa1157 pmid:33305325 pmcid:PMC7897472 fatcat:o7n7jb53vjhrxli2hxierxkz4m

A streamlined scRNA-Seq data analysis framework based on improved sparse subspace clustering

Jujuan Zhuang, Lingyu Cui, Tianqi Qu, Changjing Ren, Junlin Xu, Tianbao Li, Geng Tian, Jialiang Yang
2021 IEEE Access  
In this study, we propose a novel sparse subspace clustering method called Structured Sparse Subspace Clustering and Completion for single-cell RNA sequencing analysis by assuming the cells related together  ...  One advantage of single-cell RNA sequencing is its ability in revealing cell heterogeneity by cell clustering.  ...  Since one type of similarity usually cannot characterize all information among cells, Wang et al. developed Single-cell Interpretation via Multi-kernel Learning (SIMLR) by learning the similarities from  ... 
doi:10.1109/access.2021.3049807 fatcat:jkvzt5xwe5e5tfc6vxalx7iad4
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