scholarly journals Cross-platform Comparison of Two Pancreatic Cancer Phenotypes

2010 ◽  
Vol 9 ◽  
pp. CIN.S5755 ◽  
Author(s):  
Robert B. Scharpf ◽  
Christine A. Iacobuzio-Donahue ◽  
Leslie Cope ◽  
Ingo Ruczinski ◽  
Elizabeth Garrett-Mayer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Data ◽  
Pancreatic Cancer Cell ◽  
Expression Data ◽  
Pancreatic Cancer Cells ◽  
Technological Artifacts ◽  
Platform Comparison ◽  
Cross Platform ◽  
Cancer Phenotypes

Model-based approaches for combining gene expression data from multiple high throughput platforms can be sensitive to technological artifacts when the number of samples in each platform is small. This paper proposes simple tools for quantifying concordance in a small study of pancreatic cancer cells lines with an emphasis on visualizations that uncover intra- and inter-platform variation. Using this approach, we identify several transcripts from the integrative analysis whose over-or under-expression in pancreatic cancer cell lines was validated by qPCR.

scholarly journals HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 931 ◽  
Author(s):  
Mok ◽  
Kim ◽  
Lee ◽  
Choi ◽  
Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Structural Component ◽  
Biological Data ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Global Test ◽  
Causal Pathways

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

scholarly journals AnyExpress: Integrated toolkit for analysis of cross-platform gene expression data using a fast interval matching algorithm

2011 ◽  
Vol 12 (1) ◽  
pp. 75 ◽  
Author(s):  
Jihoon Kim ◽  
Kiltesh Patel ◽  
Hyunchul Jung ◽  
Winston P Kuo ◽  
Lucila Ohno-Machado
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Matching Algorithm ◽  
Cross Platform

scholarly journals Comparing alternative pipelines for cross-platform microarray gene expression data integration with RNA-seq data in breast cancer

2016 ◽  
Author(s):  
Alina Frolova ◽  
Vladyslav Bondarenko ◽  
Maria Obolenska
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Data Integration ◽  
Gene Expression Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Expression Data ◽  
Rna Seq ◽  
Microarray Gene Expression ◽  
Cross Platform

AbstractBackgroundAccording to major public repositories statistics an overwhelming majority of the existing and newly uploaded data originates from microarray experiments. Unfortunately, the potential of this data to bring new insights is limited by the effects of individual study-specific biases due to small number of biological samples. Increasing sample size by direct microarray data integration increases the statistical power to obtain a more precise estimate of gene expression in a population of individuals resulting in lower false discovery rates. However, despite numerous recommendations for gene expression data integration, there is a lack of a systematic comparison of different processing approaches aimed to asses microarray platforms diversity and ambiguous probesets to genes correspondence, leading to low number of studies applying integration.ResultsHere, we investigated five different approaches of the microarrays data processing in comparison with RNA-seq data on breast cancer samples. We aimed to evaluate different probesets annotations as well as different procedures of choosing between probesets mapped to the same gene. We show that pipelines rankings are mostly preserved across Affymetrix and Illumina platforms. BrainArray approach based on updated annotation and redesigned probesets definition and choosing probeset with the maximum average signal across the samples have best correlation with RNA-seq, while averaging probesets signals as well as scoring the quality of probes sequences mapping to the transcripts of the targeted gene have worse correlation. Finally, randomly selecting probeset among probesets mapped to the same gene significantly decreases the correlation with RNA-seq.ConclusionWe show that methods, which rely on actual probesets signal intensities, are advantageous to methods considering biological characteristics of the probes sequences only and that cross-platform integration of datasets improves correlation with the RNA-seq data. We consider the results obtained in this paper contributive to the integrative analysis as a worthwhile alternative to the classical meta-analysis of the multiple gene expression datasets.

scholarly journals Cross-platform proteomics to advance genetic prioritisation strategies

2021 ◽  
Author(s):  
Maik Pietzner ◽  
Eleanor Wheeler ◽  
Julia Carrasco-Zanini ◽  
Nicola D. Kerrison ◽  
Erin Oerton ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Drug Target ◽  
Large Scale ◽  
Expression Data ◽  
Protein Targets ◽  
Proteomic Data ◽  
Sufficient Power ◽  
Cross Platform ◽  
Proteomic Techniques

Discovery of protein quantitative trait loci (pQTLs) has been enabled by affinity-based proteomic techniques and is increasingly used to guide genetically informed drug target evaluation. Large-scale proteomic data are now being created, but systematic, bidirectional assessment of platform differences is lacking, restricting clinical translation. We compared genetic, technical, and phenotypic determinants of 871 protein targets measured using both aptamer- (SomaScan® Platform v4) and antibody-based (Olink) assays in up to 10,708 individuals. Correlations coefficients for overlapping protein targets varied widely (median 0.38, IQR: 0.08-0.64). We found that 64% of pQTLs were shared across both platforms among all identified 608 cis- and 1,315 trans-pQTLs with sufficient power for replication, but with correlations of effect estimates being lower than previously reported (cis: 0.41, trans: 0.34). We identified technical, protein, and variant characteristics that contributed significantly to platform differences and found contradicting phenotypic associations attributable to those. We demonstrate how integrating phenomic and gene expression data improves genetic prioritisation strategies, including platform-specific pQTLs.

scholarly journals DSGeo: Software tools for cross-platform analysis of gene expression data in GEO

2010 ◽  
Vol 43 (5) ◽  
pp. 709-715 ◽  
Author(s):  
Ronilda Lacson ◽  
Erik Pitzer ◽  
Jihoon Kim ◽  
Pedro Galante ◽  
Christian Hinske ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Software Tools ◽  
Expression Data ◽  
Cross Platform

scholarly journals Transcriptional deconvolution reveals consistent functional subtypes of pancreatic cancer epithelium and stroma

2018 ◽  
Author(s):  
Jing He ◽  
H. Carlo Maurer ◽  
Sam R. Holmstrom ◽  
Tao Su ◽  
Aqeel Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Extracellular Matrix ◽  
Gene Expression Data ◽  
Stromal Cells ◽  
Ductal Adenocarcinoma ◽  
Expression Data ◽  
Tumor Tissues ◽  
Functional Classes ◽  
Laser Capture

SummaryBulk tumor tissues comprise intermixed populations of neoplastic cells and multiple lineages of stromal cells. We used laser capture microdissection and RNA sequencing to disentangle the transcriptional programs active in the malignant epithelium and stroma of pancreatic ductal adenocarcinoma (PDA). This led to the development of a new algorithm (ADVOCATE) that accurately predicts the compartment fractions of bulk tumor samples and can computationally purify bulk gene expression data from PDA. We also present novel stromal subtypes, derived from 110 microdissected PDA stroma samples, that were enriched in extracellular matrix– and immune–associated processes. Finally, we applied ADVOCATE to systematically evaluate cross–compartment subtypes spanning four patient cohorts, revealing consistent functional classes and survival associations despite substantial compositional differences.

scholarly journals Cross platform analysis of methylation, miRNA and stem cell gene expression data in germ cell tumors highlights characteristic differences by tumor histology

BMC Cancer ◽  
2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Jenny N. Poynter ◽  
Jessica R. B. M. Bestrashniy ◽  
Kevin A. T. Silverstein ◽  
Anthony J. Hooten ◽  
Christopher Lees ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Germ Cell ◽  
Gene Expression Data ◽  
Germ Cell Tumors ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cross Platform ◽  
Cell Gene ◽  
Stem Cell Gene
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