Improving knowledge on the activation of bone marrow fibroblasts in MGUS and MM disease through the automatic extraction of genes via a nonnegative matrix factorization approach on gene expression profiles

Abstract Motivation A number of computational methods have been proposed recently to profile tumor microenvironment (TME) from bulk RNA data, and they have proved useful for understanding microenvironment differences among therapeutic response groups. However, these methods are not able to account for tumor proportion nor variable mRNA levels across cell types. Results In this article, we propose a Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution (NITUMID) framework for TME profiling that addresses these limitations. It is designed to provide robust estimates of tumor and immune cells proportions simultaneously, while accommodating mRNA level differences across cell types. Through comprehensive simulations and real data analyses, we demonstrate that NITUMID not only can accurately estimate tumor fractions and cell types’ mRNA levels, which are currently unavailable in other methods; it also outperforms most existing deconvolution methods in regular cell type profiling accuracy. Moreover, we show that NITUMID can more effectively detect clinical and prognostic signals from gene expression profiles in tumor than other methods. Availability and implementation The algorithm is implemented in R. The source code can be downloaded at https://github.com/tdw1221/NITUMID. Supplementary information Supplementary data are available at Bioinformatics online.

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eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

Cancer Informatics ◽

10.4137/cin.s13777 ◽

2014 ◽

Vol 13s2 ◽

pp. CIN.S13777 ◽

Cited By ~ 2

Author(s):

Zheng Chang ◽

Zhenjia Wang ◽

Cody Ashby ◽

Chuan Zhou ◽

Guojun Li ◽

...

Keyword(s):

Gene Expression ◽

Cancer Patients ◽

Gene Expression Data ◽

Matrix Factorization ◽

Expression Profiles ◽

Nonnegative Matrix ◽

Gene Expression Profiles ◽

Expression Data ◽

Cancer Subtypes ◽

First Choice

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checkerboard patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

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Hybrid Clustering of Single-Cell Gene Expression and Spatial Information via Integrated NMF and K-Means

Frontiers in Genetics ◽

10.3389/fgene.2021.763263 ◽

2021 ◽

Vol 12 ◽

Author(s):

Sooyoun Oh ◽

Haesun Park ◽

Xiuwei Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Expression Profiles ◽

Single Cells ◽

Nonnegative Matrix ◽

Integrated Analysis ◽

Cell Type ◽

Location Data

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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Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization

Oncogene ◽

10.1038/sj.onc.1208858 ◽

2005 ◽

Vol 24 (47) ◽

pp. 7105-7113 ◽

Cited By ~ 57

Author(s):

Kentaro Inamura ◽

Takeshi Fujiwara ◽

Yujin Hoshida ◽

Takayuki Isagawa ◽

Michael H Jones ◽

...

Keyword(s):

Gene Expression ◽

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Hierarchical Clustering ◽

Squamous Cell ◽

Matrix Factorization ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Lung Squamous Cell Carcinoma ◽

Non Negative Matrix Factorization

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Multiple Myeloma Cells Alter Adipogenesis, Increase Senescence-Related and Inflammatory Gene Transcript Expression, and Alter Metabolism in Preadipocytes

Frontiers in Oncology ◽

10.3389/fonc.2020.584683 ◽

2021 ◽

Vol 10 ◽

Author(s):

Heather Fairfield ◽

Samantha Costa ◽

Carolyne Falank ◽

Mariah Farrell ◽

Connor S. Murphy ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Bone Marrow ◽

Lipid Accumulation ◽

Expression Profiles ◽

Adipogenic Differentiation ◽

Gene Expression Profiles ◽

Mouse Bone Marrow ◽

Gene Transcript ◽

Bone Marrow Mscs

Within the bone marrow microenvironment, mesenchymal stromal cells (MSCs) are an essential precursor to bone marrow adipocytes and osteoblasts. The balance between this progenitor pool and mature cells (adipocytes and osteoblasts) is often skewed by disease and aging. In multiple myeloma (MM), a cancer of the plasma cell that predominantly grows within the bone marrow, as well as other cancers, MSCs, preadipocytes, and adipocytes have been shown to directly support tumor cell survival and proliferation. Increasing evidence supports the idea that MM-associated MSCs are distinct from healthy MSCs, and their gene expression profiles may be predictive of myeloma patient outcomes. Here we directly investigate how MM cells affect the differentiation capacity and gene expression profiles of preadipocytes and bone marrow MSCs. Our studies reveal that MM.1S cells cause a marked decrease in lipid accumulation in differentiating 3T3-L1 cells. Also, MM.1S cells or MM.1S-conditioned media altered gene expression profiles of both 3T3-L1 and mouse bone marrow MSCs. 3T3-L1 cells exposed to MM.1S cells before adipogenic differentiation displayed gene expression changes leading to significantly altered pathways involved in steroid biosynthesis, the cell cycle, and metabolism (oxidative phosphorylation and glycolysis) after adipogenesis. MM.1S cells induced a marked increase in 3T3-L1 expression of MM-supportive genes including Il-6 and Cxcl12 (SDF1), which was confirmed in mouse MSCs by qRT-PCR, suggesting a forward-feedback mechanism. In vitro experiments revealed that indirect MM exposure prior to differentiation drives a senescent-like phenotype in differentiating MSCs, and this trend was confirmed in MM-associated MSCs compared to MSCs from normal donors. In direct co-culture, human mesenchymal stem cells (hMSCs) exposed to MM.1S, RPMI-8226, and OPM-2 prior to and during differentiation, exhibited different levels of lipid accumulation as well as secreted cytokines. Combined, our results suggest that MM cells can inhibit adipogenic differentiation while stimulating expression of the senescence associated secretory phenotype (SASP) and other pro-myeloma molecules. This study provides insight into a novel way in which MM cells manipulate their microenvironment by altering the expression of supportive cytokines and skewing the cellular diversity of the marrow.

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