Correction to “A Non-Negative Matrix Factorization Based Framework for the Analysis of Multi-Class Time-Series Single-Cell RNA-Seq Data”

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.

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

10.7287/peerj.preprints.1839 ◽

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.

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

PeerJ ◽

10.7717/peerj.2888 ◽

2017 ◽

Vol 5 ◽

pp. e2888 ◽

Cited By ~ 35

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.

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

10.7287/peerj.preprints.1839v2 ◽

2016 ◽

Cited By ~ 2

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.

Download Full-text