scholarly journals NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution

2019 ◽  
Author(s):  
Daiwei Tang ◽  
Seyoung Park ◽  
Hongyu Zhao
Keyword(s):  
Tumor Microenvironment ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Expression Profiles ◽  
Mrna Level ◽  
Nonnegative Matrix ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Supplementary Information ◽  
Mrna Levels

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.

scholarly journals eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

2014 ◽  
Vol 13s2 ◽  
pp. CIN.S13777 ◽  
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.

scholarly journals Nonnegative matrix factorization integrates single-cell multi-omic datasets with partially overlapping features

2021 ◽  
Author(s):  
April R Kriebel ◽  
Joshua D Welch
Keyword(s):  
Single Cell ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
R Package ◽  
Data Driven ◽  
Rna Seq ◽  
Single Experiment ◽  
Genomic Technologies

Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges. Integration analyses often involve datasets with partially overlapping features, including both shared features that occur in all datasets and features exclusive to a single experiment. Previous computational integration approaches require that the input matrices share the same number of either genes or cells, and thus can use only shared features. To address this limitation, we derive a novel nonnegative matrix factorization algorithm for integrating single-cell datasets containing both shared and unshared features. The key advance is incorporating an additional metagene matrix that allows unshared features to inform the factorization. We demonstrate that incorporating unshared features significantly improves integration of single-cell RNA-seq, spatial transcriptomic, SHARE-seq, and cross-species datasets. We have incorporated the UINMF algorithm into the open-source LIGER R package (https://github.com/welch-lab/liger).

scholarly journals GeromiRs Are Downregulated in the Tumor Microenvironment during Colon Cancer Colonization of the Liver in a Murine Metastasis Model

2021 ◽  
Vol 22 (9) ◽  
pp. 4819
Author(s):  
Daniela Gerovska ◽  
Patricia Garcia-Gallastegi ◽  
Olatz Crende ◽  
Joana Márquez ◽  
Gorka Larrinaga ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Tumor Microenvironment ◽  
Cancer Progression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Liver Sinusoidal Endothelial Cells ◽  
Ito Cells ◽  
Metabolic Defects ◽  
Metastasis Model

Cancer is a phenomenon broadly related to ageing in various ways such as cell cycle deregulation, metabolic defects or telomerases dysfunction as principal processes. Although the tumor cell is the main actor in cancer progression, it is not the only element of the disease. Cells and the matrix surrounding the tumor, called the tumor microenvironment (TME), play key roles in cancer progression. Phenotypic changes of the TME are indispensable for disease progression and a few of these transformations are produced by epigenetic changes including miRNA dysregulation. In this study, we found that a specific group of miRNAs in the liver TME produced by colon cancer called geromiRs, which are miRNAs related to the ageing process, are significantly downregulated. The three principal cell types involved in the liver TME, namely, liver sinusoidal endothelial cells, hepatic stellate (Ito) cells and Kupffer cells, were isolated from a murine hepatic metastasis model, and the miRNA and gene expression profiles were studied. From the 115 geromiRs and their associated hallmarks of aging, which we compiled from the literature, 75 were represented in the used microarrays, 26 out of them were downregulated in the TME cells during colon cancer colonization of the liver, and none of them were upregulated. The histone modification hallmark of the downregulated geromiRs is significantly enriched with the geromiRs miR-15a, miR-16, miR-26a, miR-29a, miR-29b and miR-29c. We built a network of all of the geromiRs downregulated in the TME cells and their gene targets from the MirTarBase database, and we analyzed the expression of these geromiR gene targets in the TME. We found that Cercam and Spsb4, identified as prognostic markers in a few cancer types, are associated with downregulated geromiRs and are upregulated in the TME cells.

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

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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