scholarly journals Emerging deep learning methods for single-cell RNA-seq data analysis

2019 ◽  
Vol 7 (4) ◽  
pp. 247-254 ◽  
Author(s):  
Jie Zheng ◽  
Ke Wang
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Single Cell ◽  
Rna Seq ◽  
Learning Methods

SMILE: Mutual Information Learning for Integration of Single-cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Deep Learning ◽  
Mutual Information ◽  
Single Cell ◽  
Learning Algorithm ◽  
Cellular Systems ◽  
Supplementary Information ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Integrate Data

Abstract Motivation Deep learning approaches have empowered single-cell omics data analysis in many ways and generated new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present an unsupervised deep learning algorithm that learns discriminative representations for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Results Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the shared space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. When paired cells are known, SMILE can integrate data with unmatched feature, such as genes for RNA-seq and genome wide peaks for ATAC-seq. Integrated representations learned from joint profiling technologies can then be used as a framework for comparing independent single source data. Supplementary information Supplementary data are available at Bioinformatics online. The source code of SMILE including analyses of key results in the study can be found at: https://github.com/rpmccordlab/SMILE.

Joint CC and Bimax: A Biclustering Method for Single-Cell RNA-Seq Data Analysis

2021 ◽  
pp. 499-510
Author(s):  
He-Ming Chu ◽  
Xiang-Zhen Kong ◽  
Jin-Xing Liu ◽  
Juan Wang ◽  
Sha-Sha Yuan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Seq

Clustering single-cell RNA-seq data with a model-based deep learning approach

2019 ◽  
Vol 1 (4) ◽  
pp. 191-198 ◽  
Author(s):  
Tian Tian ◽  
Ji Wan ◽  
Qi Song ◽  
Zhi Wei
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Learning Approach ◽  
Rna Seq ◽  
Model Based

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2020 ◽  
Vol 30 (4) ◽  
pp. 611-621 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Data Analysis ◽  
Single Cell ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Rna Seq

scholarly journals Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

2019 ◽  
Vol 10 ◽  
Author(s):  
Geng Chen ◽  
Baitang Ning ◽  
Tieliu Shi
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Rna Seq ◽  
Computational Data

scholarly journals Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection

2020 ◽  
Vol 14 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Xin Zou ◽  
Ke Chen ◽  
Jiawei Zou ◽  
Peiyi Han ◽  
Jie Hao ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Potential Risk ◽  
Rna Seq ◽  
Human Organs

scholarly journals Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks

2016 ◽  
Vol 21 (9) ◽  
pp. 998-1003 ◽  
Author(s):  
Oliver Dürr ◽  
Beate Sick
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Single Cell ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Misclassification Rate ◽  
Support Vector ◽  
Learning Methods ◽  
Phenotype Classification

Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening–based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

scholarly journals Full-Length Transcriptome: A Reliable Alternative for Single-Cell RNA-Seq Analysis in the Spleen of Teleost Without Reference Genome

2021 ◽  
Vol 12 ◽  
Author(s):  
Lixing Huang ◽  
Ying Qiao ◽  
Wei Xu ◽  
Linfeng Gong ◽  
Rongchao He ◽  
...  
Keyword(s):  
Data Analysis ◽  
B Cells ◽  
Single Cell ◽  
Reference Genome ◽  
Immune Cell ◽  
Cell Types ◽  
Full Length ◽  
Cell Populations ◽  
Epinephelus Coioides ◽  
Rna Seq

Fish is considered as a supreme model for clarifying the evolution and regulatory mechanism of vertebrate immunity. However, the knowledge of distinct immune cell populations in fish is still limited, and further development of techniques advancing the identification of fish immune cell populations and their functions are required. Single cell RNA-seq (scRNA-seq) has provided a new approach for effective in-depth identification and characterization of cell subpopulations. Current approaches for scRNA-seq data analysis usually rely on comparison with a reference genome and hence are not suited for samples without any reference genome, which is currently very common in fish research. Here, we present an alternative, i.e. scRNA-seq data analysis with a full-length transcriptome as a reference, and evaluate this approach on samples from Epinephelus coioides-a teleost without any published genome. We show that it reconstructs well most of the present transcripts in the scRNA-seq data achieving a sensitivity equivalent to approaches relying on genome alignments of related species. Based on cell heterogeneity and known markers, we characterized four cell types: T cells, B cells, monocytes/macrophages (Mo/MΦ) and NCC (non-specific cytotoxic cells). Further analysis indicated the presence of two subsets of Mo/MΦ including M1 and M2 type, as well as four subsets in B cells, i.e. mature B cells, immature B cells, pre B cells and early-pre B cells. Our research will provide new clues for understanding biological characteristics, development and function of immune cell populations of teleost. Furthermore, our approach provides a reliable alternative for scRNA-seq data analysis in teleost for which no reference genome is currently available.

scholarly journals SCOUT: Single-cell outlier analysis in cancer

2020 ◽  
Author(s):  
Giovana Ravizzoni Onzi ◽  
Juliano Luiz Faccioni ◽  
Alvaro G. Alvarado ◽  
Paula Andreghetto Bracco ◽  
Harley I. Kornblum ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Biological Markers ◽  
Rna Seq ◽  
Outlier Analysis ◽  
Mass Cytometry ◽  
Wide Range ◽  
Cell Data

Outliers are often ignored or even removed from data analysis. In cancer, however, single outlier cells can be of major importance, since they have uncommon characteristics that may confer capacity to invade, metastasize, or resist to therapy. Here we present the Single-Cell OUTlier analysis (SCOUT), a resource for single-cell data analysis focusing on outlier cells, and the SCOUT Selector (SCOUTS), an application to systematically apply SCOUT on a dataset over a wide range of biological markers. Using publicly available datasets of cancer samples obtained from mass cytometry and single-cell RNA-seq platforms, outlier cells for the expression of proteins or RNAs were identified and compared to their non-outlier counterparts among different samples. Our results show that analyzing single-cell data using SCOUT can uncover key information not easily observed in the analysis of the whole population.

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