Analysis of high-throughput biological data using their rank values

2018 ◽  
Vol 28 (8) ◽  
pp. 2276-2291 ◽  
Author(s):  
Doulaye Dembélé
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
High Throughput ◽  
Expression Profiling ◽  
High Performance ◽  
Real Data ◽  
Quality Parameters ◽  
Biological Data ◽  
Data Sets ◽  
Data Set

High-throughput biological technologies are routinely used to generate gene expression profiling or cytogenetics data. To achieve high performance, methods available in the literature become more specialized and often require high computational resources. Here, we propose a new versatile method based on the data-ordering rank values. We use linear algebra, the Perron–Frobenius theorem and also extend a method presented earlier for searching differentially expressed genes for the detection of recurrent copy number aberration. A result derived from the proposed method is a one-sample Student’s t-test based on rank values. The proposed method is to our knowledge the only that applies to gene expression profiling and to cytogenetics data sets. This new method is fast, deterministic, and requires a low computational load. Probabilities are associated with genes to allow a statistically significant subset selection in the data set. Stability scores are also introduced as quality parameters. The performance and comparative analyses were carried out using real data sets. The proposed method can be accessed through an R package available from the CRAN (Comprehensive R Archive Network) website: https://cran.r-project.org/web/packages/fcros .

An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver

2004 ◽  
Vol 17 (2) ◽  
pp. 140-149 ◽  
Author(s):  
Julian L. Griffin ◽  
Stephanie A. Bonney ◽  
Chris Mann ◽  
Abdul M. Hebbachi ◽  
Geoff F. Gibbons ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fatty Liver ◽  
Gene Expression Profiling ◽  
Stress Responses ◽  
Expression Profiling ◽  
Orotic Acid ◽  
Dna Microarrays ◽  
Data Sets ◽  
Functional Genomic ◽  
H Nmr

In functional genomics, DNA microarrays for gene expression profiling are increasingly being used to provide insights into biological function or pathology. To better understand the significance of the multiple transcriptional changes across a time period, the temporal changes in phenotype must be described. Orotic acid-induced fatty liver disease was investigated at the transcriptional and metabolic levels using microarrays and metabolic profiling in two strains of rats. High-resolution 1H-NMR spectroscopic analysis of liver tissue indicated that Kyoto rats compared with Wistar rats are predisposed to the insult. Metabolite analysis and gene expression profiling following orotic acid treatment identified perturbed metabolic pathways, including those involved in fatty acid, triglyceride, and phospholipid synthesis, β-oxidation, altered nucleotide, methyl donor, and carbohydrate metabolism, and stress responses. Multivariate analysis and statistical bootstrapping were used to investigate co-responses with transcripts involved in metabolism and stress responses. This reverse functional genomic strategy highlighted the relationship between changes in the transcription of stearoyl-CoA desaturase 1 and those of other lipid-related transcripts with changes in NMR-derived lipid profiles. The results suggest that the integration of 1H-NMR and gene expression data sets represents a robust method for identifying a focused line of research in a complex system.

Breast cancer patients stratification by microarray-based gene expression profiling from FFPET samples

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22041-e22041
Author(s):  
C. Lih ◽  
Y. Li ◽  
L. Trinh ◽  
S. Chien ◽  
X. Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Gene Expression Signature ◽  
Biological Data ◽  
Clinical Samples ◽  
Breast Cancer Patients ◽  
Disease Stratification

e22041 Background: Microarrays have been used to monitor global genes expression and have aided the identification of novel biomarkers for patients stratification and drug response prediction . To date there has been limited application of microarray- based gene expression analysis to formalin fixed paraffin embedded tissues (FFPET). FFPE tissues are the most commonly available clinical samples with documented clinical information for retrospective clinical analysis. However, FFPET RNA has proven to be an obstacle for microarray analysis because of low yield and compromised RNA integrity. Methods: Using a novel RNA amplification method, Single Primer Isothermal Amplification (SPIA, NuGEN Inc, San Carlos, CA), we amplified FFPET RNA, hybridized amplified, and labeled cDNA onto Affymetrix HG U133plus2 GeneChips. Results: We found that SPIA amplification successfully overcomes the problems of poor quality of FFPET RNA, and produced informative biological data. Comparing the gene expression data from 5 different types of FFPET archival cancer samples (breast, lung, ovarian, colon, and melanoma), we demonstrated that gene expression signatures clearly distinguish the tissue of origin. Further, from an analysis of 91 FFPET samples comprised of ER+, HER2+, triple negative breast cancer patients, and normal breast tissue, we have identified a 103 gene signature that distinguishes the intrinsic sub-types of breast cancer. Finally, the accuracy of gene expression measured by microarray was verified by real time PCR quantitation of the ERBB2 gene, resulting in a significant correlation (R = 0.88). Conclusions: We have demonstrated the feasibility of global gene expression profiling using RNA extracted from FFPET and have shown that a gene expression signature can stratify patient samples into different subtypes of disease. This study paves the way to identify novel molecular biomarkers for disease stratification and therapy response from archival FFPET samples, leading to the goals of personalized medicine. No significant financial relationships to disclose.

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