Performance comparison of Affymetrix and Illumina microarray technologies_AffymetrixDataset
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE11430 | GEO | 2008/06/27
SECONDARY ACCESSION(S): PRJNA109231
REPOSITORIES: GEO
Similar Datasets
Project description:The present study was conducted to compare the ability of Affymetrix and Illumina microarray technologies to characterize the differential gene expression profiles of human monocytes and monocyte-derived-macrophages. Keywords: Cross-platform comparison
2008-06-27 | GSE10213 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.
2008-06-27 | GSE11540 | GEO
Project description:Cross-presentation of cell-associated antigens is carried out by classical DCs (cDCs) and monocyte-derived DCs (Mo-DCs), but whether a similar or distinct program exists for this process is unknown. In examining this issue, we discovered that only Ly-6ChiTremL4– monocytes, but not Ly-6ChiTremL4+ monocytes, can differentiate into Zbtb46+ Mo-DCs in response to GM-CSF and IL-4. However, Ly-6ChiTremL4+ monocytes were committed to Nur77-dependent development of Ly-6CloTremL4+ monocytes. Further, differentiation of monocytes with GM-CSF required addition of IL-4 to generate Zbtb46+ Mo-DCs that cross-presented as efficiently as CD24+ cDCs, which was accompanied by increased Batf3 and Irf4 expression. Unlike cDCs, Mo-DCs required only IRF4, and not Batf3, for cross-presentation. Further, Irf4–/– monocytes failed to develop into Zbtb46+ Mo-DCs, and instead developed into macrophages. Thus, cDCs and Mo-DCs use distinct transcriptional programs for cross-presentation that may drive different antigen-processing pathways. These differences may influence development of therapeutic DC vaccines based on Mo-DCs.
2016-05-25 | GSE75013 | GEO
Project description:BackgroundSystematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.ResultsA varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.ConclusionsThe magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.
| S-EPMC3269440 | biostudies-literature
Project description:Cross-presentation of cell-associated antigens is carried out by classical DCs (cDCs) and monocyte-derived DCs (Mo-DCs), but whether a similar or distinct program exists for this process is unknown. In examining this issue, we discovered that only Ly-6ChiTremL4– monocytes, but not Ly-6ChiTremL4+ monocytes, can differentiate into Zbtb46+ Mo-DCs in response to GM-CSF and IL-4. However, Ly-6ChiTremL4+ monocytes were committed to Nur77-dependent development of Ly-6CloTremL4+ monocytes. Further, differentiation of monocytes with GM-CSF required addition of IL-4 to generate Zbtb46+ Mo-DCs that cross-presented as efficiently as CD24+ cDCs, which was accompanied by increased Batf3 and Irf4 expression. Unlike cDCs, Mo-DCs required only IRF4, and not Batf3, for cross-presentation. Further, Irf4–/– monocytes failed to develop into Zbtb46+ Mo-DCs, and instead developed into macrophages. Thus, cDCs and Mo-DCs use distinct transcriptional programs for cross-presentation that may drive different antigen-processing pathways. These differences may influence development of therapeutic DC vaccines based on Mo-DCs.
2016-05-25 | GSE75012 | GEO
Project description:Cross-presentation of cell-associated antigens is carried out by classical DCs (cDCs) and monocyte-derived DCs (Mo-DCs), but whether a similar or distinct program exists for this process is unknown. In examining this issue, we discovered that only Ly-6ChiTremL4– monocytes, but not Ly-6ChiTremL4+ monocytes, can differentiate into Zbtb46+ Mo-DCs in response to GM-CSF and IL-4. However, Ly-6ChiTremL4+ monocytes were committed to Nur77-dependent development of Ly-6CloTremL4+ monocytes. Further, differentiation of monocytes with GM-CSF required addition of IL-4 to generate Zbtb46+ Mo-DCs that cross-presented as efficiently as CD24+ cDCs, which was accompanied by increased Batf3 and Irf4 expression. Unlike cDCs, Mo-DCs required only IRF4, and not Batf3, for cross-presentation. Further, Irf4–/– monocytes failed to develop into Zbtb46+ Mo-DCs, and instead developed into macrophages. Thus, cDCs and Mo-DCs use distinct transcriptional programs for cross-presentation that may drive different antigen-processing pathways. These differences may influence development of therapeutic DC vaccines based on Mo-DCs.
2016-05-25 | GSE75014 | GEO
Project description:BackgroundAs the use of microarray technology becomes more prevalent it is not unusual to find several laboratories employing the same microarray technology to identify genes related to the same condition in the same species. Although the experimental specifics are similar, typically a different list of statistically significant genes result from each data analysis.ResultsWe propose a statistically-based meta-analytic approach to microarray analysis for the purpose of systematically combining results from the different laboratories. This approach provides a more precise view of genes that are significantly related to the condition of interest while simultaneously allowing for differences between laboratories. Of particular interest is the widely used Affymetrix oligonucleotide array, the results of which are naturally suited to a meta-analysis. A simulation model based on the Affymetrix platform is developed to examine the adaptive nature of the meta-analytic approach and to illustrate the usefulness of such an approach in combining microarray results across laboratories. The approach is then applied to real data involving a mouse model for multiple sclerosis.ConclusionThe quantitative estimates from the meta-analysis model tend to be closer to the "true" degree of differential expression than any single lab. Meta-analytic methods can systematically combine Affymetrix results from different laboratories to gain a clearer understanding of genes' relationships to specific conditions of interest.
| S-EPMC1274254 | biostudies-literature
Project description:BACKGROUND: Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated. RESULTS: 16,097 genes common to the two platforms were retained for downstream analysis. Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman's correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; rs = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; rs = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; rs = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen's kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all rs >0.965). Immune-, stroma- and pathway-based GES showed a lower correlation relative to prognostic signatures (all rs >0.6). CONCLUSIONS: To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.
| S-EPMC4289354 | biostudies-literature
Project description:CD14+ monocytes, the predominant population in human blood, are primarily engaged in host defense and pro-inflammatory cytokine responses. Aberrant monocyte activity causes life-threatening cytokine storms, while dysfunctional monocytes lead to 'immunoparalysis.' Understanding the mechanisms controlling monocyte functions is therefore paramount. Here, we reveal platelets' vital role in human monocytes' pro-inflammatory responses. Natural low platelet counts in patients with immune thrombocytopenia (ITP) , platelet depletion in healthy human monocytes, or in vivo platelet depletion in mice, result in monocyte immunoparalysis, characterized by reduced pro-inflammatory gene expression and weakened cytokine responses to immune challenge. Remarkably, supplementation with fresh platelets reverses monocyte immunoparalysis. In mice, thrombocytopenia results in down-regulation of myeloid innate immune genes, and compromised host defense transcriptional programs in monocytes despite normal responses to LPS. Platelets control monocyte cytokines independently of traditional cross-talk pathways, acting as reservoirs of transcription factors like NF?B and MAPK p38. We pinpointed a vesicle-derived NF?B2 transfer to human monocytes by mass spectrometry-based proteomics. Functionally, platelets proportionally restored impaired cytokine secretion in human monocytes lacking MAPK p38a and NF?B p65 and NF?B2. We unveil the intercellular transfer of inflammatory regulators, positioning platelets as central checkpoints in monocyte-mediated inflammation.
2024-08-14 | PXD052113 | Pride
Project description:BackgroundMicroarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyses rapidly increasing, we expect this rate to continue. Various initiatives such as the External RNA Control Consortium (ERCC) and the MicroArray Quality Control (MAQC) project have explored ways to provide standards for the technology. For microarrays to become generally accepted as a reliable technology, statistical methods for assessing quality will be an indispensable component; however, there remains a lack of consensus in both defining and measuring microarray quality.ResultsWe begin by providing a precise definition of microarray quality and reviewing existing Affymetrix GeneChip quality metrics in light of this definition. We show that the best-performing metrics require multiple arrays to be assessed simultaneously. While such multi-array quality metrics are adequate for bench science, as microarrays begin to be used in clinical settings, single-array quality metrics will be indispensable. To this end, we define a single-array version of one of the best multi-array quality metrics and show that this metric performs as well as the best multi-array metrics. We then use this new quality metric to assess the quality of microarry data available via the Gene Expression Omnibus (GEO) using more than 22,000 Affymetrix HGU133a and HGU133plus2 arrays from 809 studies.ConclusionsWe find that approximately 10 percent of these publicly available arrays are of poor quality. Moreover, the quality of microarray measurements varies greatly from hybridization to hybridization, study to study, and lab to lab, with some experiments producing unusable data. Many of the concepts described here are applicable to other high-throughput technologies.
| S-EPMC3097162 | biostudies-literature