Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available from
Hybrid Clustering of Single-Cell Gene Expression and Spatial Information via Integrated NMF and K-Means
Advances in single cell transcriptomics have allowed us to study the identity of single cells. This has led to the discovery of new cell types and high resolution tissue maps of them. Technologies that measure multiple modalities of such data add more detail, but they also complicate data integration. We offer an integrated analysis of the spatial location and gene expression profiles of cells to determine their identity. We propose scHybridNMF (single-cell Hybrid Nonnegative Matrix Factorization), which performs cell type identification by combining sparse nonnegative matrix factorization (sparse NMF) with k-means clustering to cluster high-dimensional gene expression and low-dimensional location data. We show that, under multiple scenarios, including the cases where there is a small number of genes profiled and the location data is noisy, scHybridNMF outperforms sparse NMF, k-means, and an existing method that uses a hidden Markov random field to encode cell location and gene expression data for cell type identification.
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- Award ID(s):
- 2019771
- PAR ID:
- 10319835
- Date Published:
- Journal Name:
- Frontiers in Genetics
- Volume:
- 12
- ISSN:
- 1664-8021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract https://github.com/willtownes/nsf-paper . -
null (Ed.)Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets or transfer knowledge from one to the other to better understand cellular identity and functions. Here, we present a simple yet surprisingly effective method named common factor integration and transfer learning (cFIT) for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various datasets by a common factor space while allowing for unique distortions and shifts in genewise expression in each batch. The model parameters are learned under an iterative nonnegative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. In addition, the model enables transferring via low-rank matrix from more informative data to allow for precise identification in data of lower quality. Compared with existing approaches, our method imposes weaker assumptions on the cell composition of each individual dataset; however, it is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq datasets of developing brain from human and mouse, varying by technologies and developmental stages. The successful integration and transfer uncover the transcriptional resemblance across systems. The study helps establish a comprehensive landscape of brain cell-type diversity and provides insights into brain development.more » « less
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null (Ed.)Single cell RNA-sequencing (scRNA-seq) technology enables comprehensive transcriptomic profiling of thousands of cells with distinct phenotypic and physiological states in a complex tissue. Substantial efforts have been made to characterize single cells of distinct identities from scRNA-seq data, including various cell clustering techniques. While existing approaches can handle single cells in terms of different cell (sub)types at a high resolution, identification of the functional variability within the same cell type remains unsolved. In addition, there is a lack of robust method to handle the inter-subject variation that often brings severe confounding effects for the functional clustering of single cells. In this study, we developed a novel data denoising and cell clustering approach, namely CIBS, to provide biologically explainable functional classification for scRNA-seq data. CIBS is based on a systems biology model of transcriptional regulation that assumes a multi-modality distribution of the cells’ activation status, and it utilizes a Boolean matrix factorization approach on the discretized expression status to robustly derive functional modules. CIBS is empowered by a novel fast Boolean Matrix Factorization method, namely PFAST, to increase the computational feasibility on large scale scRNA-seq data. Application of CIBS on two scRNA-seq datasets collected from cancer tumor micro-environment successfully identified subgroups of cancer cells with distinct expression patterns of epithelial-mesenchymal transition and extracellular matrix marker genes, which was not revealed by the existing cell clustering analysis tools. The identified cell groups were significantly associated with the clinically confirmed lymph-node invasion and metastasis events across different patients. Index Terms—Cell clustering analysis, Data denoising, Boolean matrix factorization, Cancer microenvirionment, Metastasis.more » « less
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Nonnegative matrix factorization (NMF) has been increasingly popular for topic modeling of large-scale documents. However, the resulting topics often represent only general, thus redundant information about the data rather than minor, but potentially meaningful information to users. To tackle this problem, we propose a novel ensemble model of nonnegative matrix factorization for discovering high-quality local topics. Our method leverages the idea of an ensemble model to successively perform NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. The novelty of our method lies in the fact that it utilizes the residual matrix inspired by a state-of-the-art gradient boosting model and applies a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users.
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Single-cell RNA-sequencing (scRNA-seq) enables high throughput measurement of RNA expression in individual cells. Due to technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard analysis methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc combines network-regularized non-negative matrix factorization with a procedure for handling zero inflation in transcript count matrices. The matrix factorization results in a low-dimensional representation of the transcript count matrix, which imputes gene abundance for both zero and non-zero entries and can be used to cluster cells. The network regularization leverages prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be close in the low-dimensional representation. We show that netNMF-sc outperforms existing methods on simulated and real scRNA-seq data, with increasing advantage at higher dropout rates (e.g. above 60%). Furthermore, we show that the results from netNMF-sc -- including estimation of gene-gene covariance -- are robust to choice of network, with more representative networks leading to greater performance gains.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fgene.2021.763263
