Non-negative Independent Factor Analysis for single cell RNA-seq

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) enables transcriptional profiling at the level of individual cells. With the emergence of high-throughput platforms datasets comprising tens of thousands or more cells have become routine, and the technology is having an impact across a wide range of biomedical subject areas. However, scRNA-seq data are high-dimensional and affected by noise, so that scalable and robust computational techniques are needed for meaningful analysis, visualization and interpretation. Specifically, a range of matrix factorization techniques have been employed to aid scRNA-seq data analysis. In this context we note that sources contributing to biological variability between cells can be discrete (or multi-modal, for instance cell-types), or continuous (e.g. pathway activity). However, no current matrix factorization approach is set up to jointly infer such mixed sources of variability.ResultsTo address this shortcoming, we present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that combines features of complementary approaches like Independent Component Analysis (ICA), Principal Component Analysis (PCA), and Non-negative Matrix Factorization (NMF). NIFA simultaneously models uni- and multi-modal latent factors and can so isolate discrete cell-type identity and continuous pathway-level variations into separate components. Similar to NMF, NIFA constrains factor loadings to be non-negative in order to increase biological interpretability. We apply our approach to a range of data sets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA and NMF in terms of cell-type identification and biological interpretability. Studying an immunotherapy dataset in detail, we show that NIFA identifies biomedically meaningful sources of variation, derive an improved expression signature for regulatory T-cells, and identify a novel myeloid cell subtype associated with treatment response. Overall, NIFA is a general approach advancing scRNA-seq analysis capabilities and it allows researchers to better take advantage of their data. NIFA is available at https://github.com/wgmao/[email protected]

Download Full-text

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

eLife ◽

10.7554/elife.43803 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 37

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.

Download Full-text

Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq

10.1101/310599 ◽

2018 ◽

Cited By ~ 7

Author(s):

Dylan Kotliar ◽

Adrian Veres ◽

M. Aurel Nagy ◽

Shervin Tabrizi ◽

Eran Hodis ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Matrix Factorization ◽

Cell Types ◽

Rna Seq ◽

Cell Type ◽

Relative Contribution ◽

Neuronal Synapses ◽

Type Identity ◽

Brain Organoid

AbstractIdentifying 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 illustrate and enhance the use of matrix factorization as a solution to this problem. We show with simulations that a method that we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including the relative contribution of programs in each cell. Applied to published brain organoid and visual cortex scRNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected (e.g. cell cycle and hypoxia) and intriguing novel activity programs. We propose that one of the novel programs may reflect a neurosecretory phenotype and a second may underlie the formation of neuronal synapses. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types.

Download Full-text

Traits versus Issues: Factor versus Ideal-Point Analysis of Candidate Thermometer Ratings

Political Analysis ◽

10.1093/pan/2.1.97 ◽

1990 ◽

Vol 2 ◽

pp. 97-129 ◽

Cited By ~ 15

Author(s):

Henry E. Brady

Keyword(s):

Factor Analysis ◽

Ideal Point ◽

Generalized Least Squares ◽

Analysis Model ◽

Factor Analysis Model ◽

Statistical Framework ◽

Point Analysis ◽

Wide Range ◽

Voting Decisions ◽

The Ideal

This article describes a computationally simple, statistically consistent, reasonably efficient, and statistically informative generalized least squares (GLS) estimator for a general class of nonlinear, multidimensional scaling (MDS) models including the “ideal-point” models of voters' and legislators' behavior proposed by Melvin Hinich, Keith Poole, and others. Unlike other methods, the method described in this article provides a statistical framework for testing a wide range of hypotheses about these models including their functional form, their dimensionality, and the values of specific parameters. The Hinich ideal-point model is estimated using this method. It fits the data remarkably well compared to a standard factor analysis model that does not provide a reasonable fit to the data. This has the substantive implication of suggesting that voters base their voting decisions upon ideal-point dimensions like liberalism-conservatism and not upon factor analysis dimensions like competence and leadership.

Download Full-text

Dimensionality reduction based on parallel factor analysis model and independent component analysis method

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.13.014532 ◽

2019 ◽

Vol 13 (01) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Ronghua Yan ◽

Jinye Peng ◽

Dongmei Ma

Keyword(s):

Factor Analysis ◽

Independent Component Analysis ◽

Dimensionality Reduction ◽

Component Analysis ◽

Independent Component ◽

Analysis Method ◽

Analysis Model ◽

Factor Analysis Model ◽

Independent Component Analysis Method ◽

Parallel Factor

Download Full-text

NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing

10.1101/2020.09.23.308932 ◽

2020 ◽

Author(s):

Brian Aevermann ◽

Yun Zhang ◽

Mark Novotny ◽

Trygve Bakken ◽

Jeremy Miller ◽

...

Keyword(s):

Machine Learning ◽

Single Cell ◽

Rna Sequencing ◽

Marker Gene ◽

Cell Types ◽

Biological Research ◽

Marker Genes ◽

Cell Type ◽

Type Identity ◽

Wide Range

AbstractSingle cell genomics is rapidly advancing our knowledge of cell phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range of organisms and tissues are currently underway. As a result, it is critical that the cell transcriptional phenotypes discovered are defined and disseminated in a consistent and concise manner. Molecular biomarkers have historically played an important role in biological research, from defining immune cell-types by surface protein expression to defining diseases by molecular drivers. Here we describe a machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages the non-linear attributes of random forest feature selection and a binary expression scoring approach to discover the minimal marker gene expression combinations that precisely captures the cell type identity represented in the complete scRNA-seq transcriptional profiles. The marker genes selected provide a barcode of the necessary and sufficient characteristics for semantic cell type definition and serve as useful tools for downstream biological investigation. The use of NS-Forest to identify marker genes for human brain middle temporal gyrus cell types reveals the importance of cell signaling and non-coding RNAs in neuronal cell type identity.

Download Full-text