Affymetrix Arrays Interrogated with Tumor/Normal Pancreatic Samples
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Chen Nanyue
PROVIDER: S-ECPF-GEOD-22780 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:To identify genes in the chromosome 3p12 pathway to tumorigenesis in pancreatic cancer, we screened two expression platforms and then as a third platform interrogated pancreatic cancer tumor/normal samples for differential expression. Data from the third expression platform experiments are shown herein.
2011-07-01 | GSE22780 | GEO
Project description:To identify genes in the chromosome 3p12 pathway to tumorigenesis in pancreatic cancer, we screened two expression platforms and then as a third platform interrogated pancreatic cancer tumor/normal samples for differential expression. Data from the third expression platform experiments are shown herein. Frozen tumor and adjacent normal tissue were obtained from untreated, retrospective pancreatic adenocarcinoma samples available from the M. D. Anderson Cancer Center tumor bank and our collaborator (MLF). Total RNA from matched tumor/adjacent normal samples (8 paired samples) was utilized to interrogate the GeneChip U133 plus 2.0 array and resultant bioinformatic analysis performed to identify differentially expressed sequences across all three platforms.
2011-07-01 | E-GEOD-22780 | biostudies-arrayexpress
Project description:MotivationMicroarray designs have become increasingly probe-rich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts.ResultsTo study cross-hybridization, we map Affymetrix exon array probes to a set of annotated mRNA transcripts, allowing a small number of mismatches or insertion/deletions between the two sequences. Based on a systematic study of the degree to which probes with a given match type to a transcript are affected by cross-hybridization, we developed a strategy to correct for cross-hybridization biases of gene-level expression estimates. Comparison with Solexa ultra high-throughput sequencing data demonstrates that correction for cross-hybridization leads to a significant improvement of gene expression estimates.AvailabilityWe provide mappings between human and mouse exon array probes and off-target transcripts and provide software extending the GeneBASE program for generating gene-level expression estimates including the cross-hybridization correction http://biogibbs.stanford.edu/~kkapur/GeneBase/.
| S-EPMC2639301 | biostudies-literature
Project description:BackgroundAffymetrix GeneChips are an important tool in many facets of biological research. Recently, notable design changes to the chips have been made. In this study, we use publicly available data from Affymetrix to gauge the performance of three human gene expression arrays: Human Genome U133 Plus 2.0 (U133), Human Exon 1.0 ST (HuEx) and Human Gene 1.0 ST (HuGene).ResultsWe studied probe-, exon- and gene-level reproducibility of technical and biological replicates from each of the 3 platforms. The U133 array has larger feature sizes so it is no surprise that probe-level variances are smaller, however the larger number of probes per gene on the HuGene array seems to produce gene-level summaries that have similar variances. The gene-level summaries of the HuEx array are less reproducible than the other two, despite having the largest average number of probes per gene. Greater than 80% of the content on the HuEx arrays is expressed at or near background. Biological variation seems to have a smaller effect on U133 data. Comparing the overlap of differentially expressed genes, we see a high overall concordance among all 3 platforms, with HuEx and HuGene having greater overlap, as expected given their design. We performed an analysis of detection rates and area under ROC curves using an experiment made up of several mixtures of 2 human tissues. Though it appears that the HuEx array has worse performance in terms of detection rates, all arrays have similar ability to separate differentially expressed and non-differentially expressed genes.ConclusionDespite noticeable differences in the probe-level reproducibility, gene-level reproducibility and differential expression detection are quite similar across the three platforms. The HuEx array, an all-encompassing array, has the flexibility of measuring all known or predicted exonic content. However, the HuEx array induces poorer reproducibility for genes with fewer exons. The HuGene measures just the well-annotated genome content and appears to perform well. The U133 array, though not able to measure across the full length of a transcript, appears to perform as well as the newer designs on the set of genes common to all 3 platforms.
| S-EPMC2216046 | biostudies-literature
Project description:Microarray data is subject to noise and systematic variation that negatively affects the resolution of copy number analysis. We describe Rawcopy, an R package for processing of Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 microarray raw intensities (CEL files). Noise characteristics of a large number of reference samples are used to estimate log ratio and B-allele frequency for total and allele-specific copy number analysis. Rawcopy achieves better signal-to-noise ratio and higher proportion of validated alterations than commonly used free and proprietary alternatives. In addition, Rawcopy visualizes each microarray sample for assessment of technical quality, patient identity and genome-wide absolute copy number states. Software and instructions are available at http://rawcopy.org.
| S-EPMC5086940 | biostudies-literature
Project description: Not available
| S-EPMC2323405 | biostudies-literature
Project description:BACKGROUND: Copy number variation (CNV) is essential to understand the pathology of many complex diseases at the DNA level. Affymetrix SNP arrays, which are widely used for CNV studies, significantly depend on accurate copy number (CN) estimation. Nevertheless, CN estimation may be biased by several factors, including cross-hybridization and training sample batch, as well as genomic waves of intensities induced by sequence-dependent hybridization rate and amplification efficiency. Since many available algorithms only address one or two of the three factors, a high false discovery rate (FDR) often results when identifying CNV. Therefore, we have developed a new CNV detection pipeline which is based on hybridization and amplification rate correction (CNVhac). METHODS: CNVhac first estimates the allelic concentrations (ACs) of target sequences by using the sample independent parameters trained through physicochemical hybridization law. Then the raw CN is estimated by taking the ratio of AC to the corresponding average AC from a reference sample set for one specific site. Finally, a hidden Markov model (HMM) segmentation process is implemented to detect CNV regions. RESULTS: Based on public HapMap data, the results show that CNVhac effectively smoothes the genomic waves and facilitates more accurate raw CN estimates compared to other methods. Moreover, CNVhac alleviates, to a certain extent, the sample dependence of inference and makes CNV calling with appreciable low FDRs. CONCLUSION: CNVhac is an effective approach to address the common difficulties in SNP array analysis, and the working principles of CNVhac can be easily extended to other platforms.
| S-EPMC3428662 | biostudies-literature
Project description:The experimental spike-in studies of microarray hybridization conducted by Affymetrix demonstrate a nonlinear response of fluorescence intensity signal to target concentration. Several theoretical models have been put forward to explain these data. It was shown that the Langmuir adsorption isotherm recapitulates a general trend of signal response to concentration. However, this model fails to explain some key properties of the observed signal. In particular, according to the simple Langmuir isotherm, all probes should saturate at the same intensity level. However, this effect was not observed in the publicly available Affymetrix spike-in data sets. On the contrary, it was found that the saturation intensities vary greatly and can be predicted based on the probe sequence composition. In our experimental study, we attempt to account for the unexplained variation in the observed probe intensities using customized fluidics scripts. We explore experimentally the effect of the stringent wash, target concentration and hybridization time on the final microarray signal. The washing effect is assessed by scanning chips both prior to and after the stringent wash. Selective labeling of both specific and non-specific targets allows the visualization and investigation of the washing effect for both specific and non-specific signal components. We propose a new qualitative model of the probe-target hybridization mechanism that is in agreement with observed hybridization and washing properties of short oligonucleotide microarrays. This study demonstrates that desorption of incompletely bound targets during the washing cycle contributes to the observed difference in saturation levels.
| S-EPMC1919478 | biostudies-literature
Project description:Whole genome analysis on normal karyotype lung tumors from heavy smokers. Our study regarded a population of 5203 heavy smokers volunteers, enrolled in a screening program set up at the European Institute of Oncology. Each volunteer yearly undergoes low dose CT-scan. At the end of the second year, among 89 screening-identified patients with lung cancers, 57 cases with clinical localized lung malignancy were evaluated for the present analysis. These, together with 21 other asymptomatic patients (pilot population), carrying non-screening detected lung cancers, but with same characteristics of the screened population (asymptomatic, smokers, older than 50, with an occasionally detected localized cancer), constituted our study population (78 cases). At the time of surgical resection, a portion of the tumoral tissue was collected, snap-frozen and stored at -80 C, for the subsequent tumor cellularity evaluation and DNA extraction. Tumor cellularity was determined by analyzing ematossilin-eosin stained sections of the tumor samples. The evaluation was carried out by a pathologist of the Molecular Pathology Unit, at the IFOM-IEO campus. Only NK samples with an evaluated tumor cellularity of at least 70% were used for the high-resolution genomic analysis: this allowed us to restrict normal cells contamination of the tumor genome pattern in our analysis. A genomic macroscopic analysis was carried out in order to identify tumors harboring a normal karyotype, which were then subjected to a high-resolution genomic analysis, through the Affymetrix SNPs technology.
| S-ECPF-MEXP-2450 | biostudies-other
Project description:BACKGROUND: Alternative splicing and isoform level expression profiling is an emerging field of interest within genomics. Splicing sensitive microarrays, with probes targeted to individual exons or exon-junctions, are becoming increasingly popular as a tool capable of both expression profiling and finer scale isoform detection. Despite their intuitive appeal, relatively little is known about the performance of such tools, particularly in comparison with more traditional 3' targeted microarrays. Here, we use the well studied Microarray Quality Control (MAQC) dataset to benchmark the Affymetrix Exon Array, and compare it to two other popular platforms: Illumina, and Affymetrix U133. RESULTS: We show that at the gene expression level, the Exon Array performs comparably with the two 3' targeted platforms. However, the interplatform correlation of the results is slightly lower than between the two 3' arrays. We show that some of the discrepancies stem from the RNA amplification protocols, e.g. the Exon Array is able to detect expression of non-polyadenylated transcripts. More importantly, we show that many other differences result from the ability of the Exon Array to monitor more detailed isoform-level changes; several examples illustrate that changes detected by the 3' platforms are actually isoform variations, and that the nature of these variations can be resolved using Exon Array data. Finally, we show how the Exon Array can be used to detect alternative isoform differences, such as alternative splicing, transcript termination, and alternative promoter usage. We discuss the possible pitfalls and false positives resulting from isoform-level analysis. CONCLUSION: The Exon Array is a valuable tool that can be used to profile gene expression while providing important additional information regarding the types of gene isoforms that are expressed and variable. However, analysis of alternative splicing requires much more hands on effort and visualization of results in order to correctly interpret the data, and generally results in considerably higher false positive rates than expression analysis. One of the main sources of error in the MAQC dataset is variation in amplification efficiency across transcripts, most likely caused by joint effects of elevated GC content in the 5' ends of genes and reduced likelihood of random-primed first strand synthesis in the 3' ends of genes. These effects are currently not adequately corrected using existing statistical methods. We outline approaches to reduce such errors by filtering out potentially problematic data.
| S-EPMC2585104 | biostudies-literature