Identification of More Feasible MicroRNA–mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction

2016

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Abstract-Wilms tumor is one of lethal child renal cancers, for which no known disease causing mechanisms exist. In this paper, we tried to identify possible disease causing microRNA(miRNA)-mRNA pairs (interactions) by analyzing (partially matched) miRNA/mRNA gene expression profiles with the recently proposed principal component analysis based unsupervised feature extraction. It successfully identified multiple miRNA-mRNA pairs whose biological natures are convincing. Correlation coefficients between miRNA and mRNA expression in matched parts of profiles turned out to be significantly negative. Constructed miRNA-mRNA network will be a key to understand Wilms tumor causing mechanisms.

Section: Significant Negative Correlation Between Mirna-mrna Pairsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

microRNA-mRNA interaction identification in Wilms tumor using principal component analysis based unsupervised feature extraction

2016

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“…The various unsupervised gene selection methods were invented, e.g., highly variable genes, bimodal genes and dpFeature and principal component analysis (PCA) based unsupervised feature extraction (FE) (50, 34, 49, 42, 25, 31, 27, 40, 47, 4, 5, 44, 45, 13, 12, 11, 51, 39, 43, 22, 23, 24, 26, 41, 48). Chen et al (2) recently compared genes selected by these methods and concluded that the genes selected are very diverse and have their own (unique) biological features.…”

Section: Introductionmentioning

confidence: 99%

Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Turki

2019

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3Although single cell RNA sequencing (scRNA-seq) technology is newly invented and promising 4 one, because of lack of enough information that labels individual cells, it is hard to interpret 5 the obtained gene expression of each cell. Because of this insufficient information available, 6 unsupervised clustering, e.g., tSNE and UMAP, is usually employed to obtain low dimensional 7 embedding that can help to understand cell-cell relationship. One possible drawback of this 8 strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. 9 In order to fulfill this requirements, there are many methods that performed unsupervised gene 10 selection. In this study, a tensor decomposition (TD) based unsupervised feature extraction 11 (FE) was applied to the integration of two scRNA-seq expression profiles that measure human 12 and mouse midbrain development. TD based unsupervised FE could not only select coincident 13 genes between human and mouse, but also biologically reliable genes. Coincidence between two 14 species as well as biological reliability of selected genes is increased compared with principal 15 component analysis (PCA) based FE applied to the same data set in the previous study. Since 16 PCA based unsupervised FE outperformed other three popular unsupervised gene selection 17 methods, highly variable genes, bimodal genes and dpFeature, TD based unsupervised FE can 18 do so as well. In addition to this, ten transcription factors (TFs) that might regulate selected genes 19 and might contribute to midbrain development are identified. These ten TFs, BHLHE40, EGR1, 20 GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to 21 be related to brain functions and diseases. TD based unsupervised FE is promising method to 22 integrate two scRNA-seq profiles effectively. 23 Keywords: tensor decomposition, enrichment analysis, sngle cell RNA-sequencing, midbrain development, inter-species analysis 24 INTRODUCTIONSingle cell RNA sequencing (scRNA-seq) (17) is a newly invented technology that enables us to measure 25 amount of RNA in single cell basis. In spite of its promising potential, it is not easy to interpret the 26 measurements. The primary reason of this difficulty is the lack of sufficient information that characterizes 27 1 Y-h. Taguchi TD based FE to single-cell individual cells. In contrast to the huge number of cells measured, which is often as many as several 28 thousands, the number of labeling is limited, e.g., measurement of conditions as well as the amount of 29 expression of key genes measured by fluorescence-activated cell sorting, whose number is typically as little 30 as tens. This prevents us from selecting genes that characterize the individual cell properties. 31In order to deal with samples without suitable number of labelling, unsupervised method is frequently 32 used, since it does not make use of labeling information directly. K-means clustering as well as hierarchical 33 clustering are the popular methodology tha...

“…In this paper, I propose a strategy that can infer drug candidates from drug treatmentassociated gene expression profiles without the information about known compounds for diseases. In this strategy, I employ the tensor decomposition (TD)-based unsupervised feature extraction (FE) approach, which is an extension of the recently proposed principal component analysis (PCA)-based unsupervised FE; PCA-based unsupervised FE successfully solved various bioinformatic problems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In this TD-based strategy, tensors were generated using a mathematical product of a gene expression profile of drug-treated cell lines and of a gene expression profile of a disease.…”

Section: Introductionmentioning

confidence: 99%

Identification of Candidate Drugs for Heart Failure using Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Integrated Analysis of Gene Expression between Heart Failure and DrugMatrix Datasets

2017

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Abstract.Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets not mRNAs but proteins, mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I apply tensor decomposition-based unsupervised feature extraction to the integrated analysis of gene expression between heart failure and the DrugMatrix dataset where comprehensive data on gene expression during various drug treatments of rats were reported. I found that this strategy, in a fully unsupervised manner, enables us to identify a combined set of genes and compounds, for which various associations with heart failure were reported. Keywords: Tensor decomposition ‧ Drug discovery ‧ Heart diseases IntroductionIn silico drug discovery is an important task because experimental identification/verification of therapeutic compounds is a time-consuming and expensive process. There are two major trends of in silico drug discovery: the ligand-based approach [1] and structure-based [2] approach. The former is very straightforward; new drug candidates are identified based upon the similarity with known drugs no matter how the similarity is evaluated. Although it is a powerful method, there are some drawbacks; if there are no known drugs for target proteins, then there is no way to find new drug candidates. Even if there are many known drugs for the target protein, it is hopeless to find compounds that are effective but without any similarity with known drugs. The second, structure-based approach, can address these weaknesses. It can identify new therapeutic compounds even without the information about known drugs. Of course, there are some drawbacks in this strategy, too. If the target protein structure is not known, it must be predicted prior to the drug discovery process. Even if the target protein's structure is known, because we need to numerically verify the binding affinity between the ligand compound and target protein, which also requires a large amount of computational resources, structure-based in silico drug discovery is still far from easy to perform. In addition, prediction accuracy of protein structure and of ligand-binding structure is not . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.