scholarly journals DeTOKI identifies and characterizes the dynamics of chromatin TAD-like domains in a single cell

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiao Li ◽  
Guangjie Zeng ◽  
Angsheng Li ◽  
Zhihua Zhang
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Single Cells ◽  
Topologically Associating Domains ◽  
Mammalian Genomes ◽  
Non Negative Matrix Factorization

AbstractTopologically associating domains (TAD) are a key structure of the 3D mammalian genomes. However, the prevalence and dynamics of TAD-like domains in single cells remain elusive. Here we develop a new algorithm, named deTOKI, to decode TAD-like domains with single-cell Hi-C data. By non-negative matrix factorization, deTOKI seeks regions that insulate the genome into blocks with minimal chance of clustering. deTOKI outperforms competing tools and reliably identifies TAD-like domains in single cells. Finally, we find that TAD-like domains are not only prevalent, but also subject to tight regulation in single cells.

scholarly journals Fast and robust non-negative matrix factorization for single-cell experiments

2021 ◽  
Author(s):  
Zachary J. DeBruine ◽  
Karsten Melcher ◽  
Timothy J. Triche
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Principal Component ◽  
R Package ◽  
Complex Data ◽  
Technological Advances ◽  
Value Decomposition ◽  
Non Negative Matrix Factorization

AbstractNon-negative matrix factorization (NMF) is an intuitively appealing method to extract additive combinations of measurements from noisy or complex data. NMF is applied broadly to text and image processing, time-series analysis, and genomics, where recent technological advances permit sequencing experiments to measure the representation of tens of thousands of features in millions of single cells. In these experiments, a count of zero for a given feature in a given cell may indicate either the absence of that feature or an insufficient read coverage to detect that feature (“dropout”). In contrast to spectral decompositions such as the Singular Value Decomposition (SVD), the strictly positive imputation of signal by NMF is an ideal fit for single-cell data with ambiguous zeroes. Nevertheless, most single-cell analysis pipelines apply SVD or Principal Component Analysis (PCA) on transformed counts because these implementations are fast while current NMF implementations are slow. To address this need, we present an accessible NMF implementation that is much faster than PCA and rivals the runtimes of state-of-the-art SVD. NMF models learned with our implementation from raw count matrices yield intuitive summaries of complex biological processes, capturing coordinated gene activity and enrichment of sample metadata. Our NMF implementation, available in the RcppML (Rcpp Machine Learning library) R package, improves upon current NMF implementations by introducing a scaling diagonal to enable convex L1 regularization for feature engineering, reproducible factor scalings, and symmetric factorizations. RcppML NMF easily handles sparse datasets with millions of samples, making NMF an attractive replacement for PCA in the analysis of single-cell experiments.

scholarly journals Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Dylan Kotliar ◽  
Adrian Veres ◽  
M Aurel Nagy ◽  
Shervin Tabrizi ◽  
Eran Hodis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Matrix Factorization ◽  
Cell Types ◽  
Environmental Cues ◽  
Rna Seq ◽  
Cell Type ◽  
Type Identity ◽  
Brain Organoid ◽  
Non Negative Matrix Factorization

Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here, we benchmark and enhance the use of matrix factorization to solve this problem. We show with simulations that a method we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including their relative contributions in each cell. To illustrate the insights this approach enables, we apply it to published brain organoid and visual cortex scRNA-Seq datasets; cNMF refines cell types and identifies both expected (e.g. cell cycle and hypoxia) and novel activity programs, including programs that may underlie a neurosecretory phenotype and synaptogenesis.

scholarly journals Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders

2019 ◽  
Author(s):  
Soeren Lukassen ◽  
Foo Wei Ten ◽  
Roland Eils ◽  
Christian Conrad
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Network Model ◽  
Neural Network Model ◽  
Matrix Factorization ◽  
Latent Variable ◽  
Single Cells ◽  
Sequencing Data ◽  
Gene Set ◽  
Gene Sets

AbstractRecent advances in single-cell RNA sequencing (scRNA-Seq) have driven the simultaneous measurement of the expression of 1,000s of genes in 1,000s of single cells. These growing data sets allow us to model gene sets in biological networks at an unprecedented level of detail, in spite of heterogenous cell populations. Here, we propose an unsupervised deep neural network model that is a hybrid of matrix factorization and conditional variational autoencoders (CVA), which utilizes weights as matrix factorizations to obtain gene sets, while class-specific inputs to the latent variable space facilitate a plausible identification of cell types. This artificial neural network model seamlessly integrates functional gene set inference, experimental batch effect correction, and static gene identification, which we conceptually prove here for three single-cell RNA-Seq datasets and suggest for future single-cell-gene analytics.

scholarly journals Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

2016 ◽  
Author(s):  
Xun Zhu ◽  
Travers Ching ◽  
Xinghua Pan ◽  
Sherman Weissman ◽  
Lana Garmire
Keyword(s):  
Single Cell ◽  
Hierarchical Clustering ◽  
Matrix Factorization ◽  
New Technology ◽  
Data Sets ◽  
Biological Processes ◽  
Rna Seq ◽  
Clustering Methods ◽  
Hematopoietic Stem ◽  
Non Negative Matrix Factorization

Single-cell RNA-Sequencing (scRNA-Seq) is a cutting edge technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we have investigated the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq data sets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy even when the clustering results of K-means and hierarchical clustering are enhanced by t-SNE. Moreover, NMF successfully detect the subpopulations, such as those in a single glioblastoma patient. Furthermore, in conjugation with the modularity detection method FEM, it reveals unique modules that are indicative of clinical subtypes. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data, and the NMFEM pipeline is suitable for modularity detection among single-cell RNA-Seq data.

scholarly journals Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2888 ◽  
Author(s):  
Xun Zhu ◽  
Travers Ching ◽  
Xinghua Pan ◽  
Sherman M. Weissman ◽  
Lana Garmire
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Detection Method ◽  
New Technology ◽  
Biological Processes ◽  
Rna Seq ◽  
Clustering Methods ◽  
Hematopoietic Stem ◽  
Protein Protein Interaction ◽  
Non Negative Matrix Factorization

Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at:https://github.com/lanagarmire/NMFEM.

scholarly journals Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Shuqin Zhang ◽  
Liu Yang ◽  
Jinwen Yang ◽  
Zhixiang Lin ◽  
Michael K Ng
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Rna Sequencing ◽  
Matrix Factorization ◽  
Sparse Matrix ◽  
Differential Expression Analysis ◽  
Data Matrix ◽  
Research Attention ◽  
Single Cell Rna Sequencing ◽  
Non Negative Matrix Factorization

Abstract Single cell RNA-sequencing (scRNA-seq) technology, a powerful tool for analyzing the entire transcriptome at single cell level, is receiving increasing research attention. The presence of dropouts is an important characteristic of scRNA-seq data that may affect the performance of downstream analyses, such as dimensionality reduction and clustering. Cells sequenced to lower depths tend to have more dropouts than those sequenced to greater depths. In this study, we aimed to develop a dimensionality reduction method to address both dropouts and the non-negativity constraints in scRNA-seq data. The developed method simultaneously performs dimensionality reduction and dropout imputation under the non-negative matrix factorization (NMF) framework. The dropouts were modeled as a non-negative sparse matrix. Summation of the observed data matrix and dropout matrix was approximated by NMF. To ensure the sparsity pattern was maintained, a weighted ℓ1 penalty that took into account the dependency of dropouts on the sequencing depth in each cell was imposed. An efficient algorithm was developed to solve the proposed optimization problem. Experiments using both synthetic data and real data showed that dimensionality reduction via the proposed method afforded more robust clustering results compared with those obtained from the existing methods, and that dropout imputation improved the differential expression analysis.

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