Microarray gene expression: A study of between-platform association of Affymetrix and cDNA arrays

Sarmah, Chintanu; Samarasinghe, Sandhya

doi:10.1016/j.compbiomed.2011.08.007

Cited by 20 publications

(11 citation statements)

References 27 publications

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“…The gene symbols or gene names corresponding to some probe set IDs were empty in microarrays because some genes were still unknown or unavailable. There were 22283 probe set IDs in Affymetrix Human Genome U133A Array named platform of GPL96, and 54675 probe set IDs in Affymetrix Human Genome U133 Plus 2.0 named platform of GPL570 (Sarmah and Samarasinghe, 2011).…”

Section: Affymetrix Microarraysmentioning

confidence: 99%

Expression Profiles of Loneliness-associated Genes for Survival Prediction in Cancer Patients

You

Yeh

Su³

2014

Asian Pacific Journal of Cancer Prevention

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Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for highlonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Lonelinessassociated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

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Section: Affymetrix Microarraysmentioning

confidence: 99%

Expression Profiles of Loneliness-associated Genes for Survival Prediction in Cancer Patients

You

Yeh

Su³

2014

Asian Pacific Journal of Cancer Prevention

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“…With the advent of postgenome era in bioinformatics research, vast quantities of genomic data are being generated by DNA-microarray and deep-sequencing techniques [3][4][5][6]. Because these techniques can concomitantly profile thousands of genes, these genomic expression data produced by these technologies can fully reflect the transcription activity at a certain point, which affords researchers' avenues to understand and study life mechanism in genome-wide range.…”

Section: Introductionmentioning

confidence: 99%

Block-Constraint Laplacian-Regularized Low-Rank Representation and Its Application for Cancer Sample Clustering Based on Integrated TCGA Data

et al. 2020

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Low-Rank Representation (LRR) is a powerful subspace clustering method because of its successful learning of low-dimensional subspace of data. With the breakthrough of “OMics” technology, many LRR-based methods have been proposed and used to cancer clustering based on gene expression data. Moreover, studies have shown that besides gene expression data, some other genomic data in TCGA also contain important information for cancer research. Therefore, these genomic data can be integrated as a comprehensive feature source for cancer clustering. How to establish an effective clustering model for comprehensive analysis of integrated TCGA data has become a key issue. In this paper, we develop the traditional LRR method and propose a novel method named Block-constraint Laplacian-Regularized Low-Rank Representation (BLLRR) to model multigenome data for cancer sample clustering. The proposed method is dedicated to extracting more abundant subspace structure information from multiple genomic data to improve the accuracy of cancer sample clustering. Considering the heterogeneity of different genome data, we introduce the block-constraint idea into our method. In BLLRR decomposition, we treat each genome data as a data block and impose different constraints on different data blocks. In addition, graph Laplacian is also introduced into our method to better learn the topological structure of data by preserving the local geometric information. The experiments demonstrate that the BLLRR method can effectively analyze integrated TCGA data and extract more subspace structure information from multigenome data. It is a reliable and efficient clustering algorithm for cancer sample clustering.

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“…With the development of DNA microarray technology, it is possible for biologists to monitor the expression of thousands of genes simultaneously [ 1 , 2 ]. Besides, these genes have been detected more comprehensively than ever before.…”

Section: Introductionmentioning

confidence: 99%

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

Liu

Gao

et al. 2015

PLoS ONE

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In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.

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Microarray gene expression: A study of between-platform association of Affymetrix and cDNA arrays

Cited by 20 publications

References 27 publications

Expression Profiles of Loneliness-associated Genes for Survival Prediction in Cancer Patients

Expression Profiles of Loneliness-associated Genes for Survival Prediction in Cancer Patients

Block-Constraint Laplacian-Regularized Low-Rank Representation and Its Application for Cancer Sample Clustering Based on Integrated TCGA Data

A P-Norm Robust Feature Extraction Method for Identifying Differentially Expressed Genes

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