Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells.
Performance comparison of two microarray platforms to assess differential gene expression in human monocyte and macrophage cells.
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DOI：
10.1186/1471-2164-9-302
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发表时间：
2008-06-25
期刊：
影响因子：
4.4
通讯作者：
Cambien, Francois
中科院分区：
文献类型：
--
作者：
Maouche, Seraya;Poirier, Odette;Godefroy, Tiphaine;Olaso, Robert;Gut, Ivo;Collet, Jean-Phillipe;Montalescot, Gilles;Cambien, Francois
在这项研究中，我们评估了各自的能力，Affyphase和Illumina微阵列方法来回答相关的生物学问题，即基因表达的变化之间的休息单核细胞和巨噬细胞来源于这些单核细胞。将每种类型细胞的五个RNA样品平行杂交到两个平台。此外，使用RNG/MRC双色平台从大量杂交（来自86个个体的mRNA）中生成差异表达基因（DEG）的参考列表。我们的研究结果显示了一个重要的重叠的Illumina和Affyphidae DEG名单。此外，这些列表中超过70%的基因也存在于参考列表中。总体而言，两个平台在生物学意义方面具有非常相似的性能，通过DEG列表中存在属于与单核细胞和巨噬细胞生物学相关的基因本体论（GO）类别的过量基因进行评估。我们的研究结果支持微阵列质量控制（MAQC）项目的结论，即用于构成DEG列表的标准强烈影响平台之间的一致性程度。然而，我们的数据不支持MAQC作者推荐的通过效应大小（倍数变化）而不是统计学显著性（p值）来优先考虑基因以增强跨平台再现性的重要性。基于GO富集的功能分析表明，2种比较技术提供了非常相似的结果，并识别了参考列表中富集的大多数相关GO类别。
In this study we assessed the respective ability of Affymetrix and Illumina microarray methodologies to answer a relevant biological question, namely the change in gene expression between resting monocytes and macrophages derived from these monocytes. Five RNA samples for each type of cell were hybridized to the two platforms in parallel. In addition, a reference list of differentially expressed genes (DEG) was generated from a larger number of hybridizations (mRNA from 86 individuals) using the RNG/MRC two-color platform. Our results show an important overlap of the Illumina and Affymetrix DEG lists. In addition, more than 70% of the genes in these lists were also present in the reference list. Overall the two platforms had very similar performance in terms of biological significance, evaluated by the presence in the DEG lists of an excess of genes belonging to Gene Ontology (GO) categories relevant for the biology of monocytes and macrophages. Our results support the conclusion of the MicroArray Quality Control (MAQC) project that the criteria used to constitute the DEG lists strongly influence the degree of concordance among platforms. However the importance of prioritizing genes by magnitude of effect (fold change) rather than statistical significance (p-value) to enhance cross-platform reproducibility recommended by the MAQC authors was not supported by our data. Functional analysis based on GO enrichment demonstrates that the 2 compared technologies delivered very similar results and identified most of the relevant GO categories enriched in the reference list.
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影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
46.9
作者：
Canales, Roger D.;Luo, Yuling;Goodsaid, Federico M.
通讯作者：
Goodsaid, Federico M.
影响因子：
14.9
作者：
Barnes M;Freudenberg J;Thompson S;Aronow B;Pavlidis P
通讯作者：
Pavlidis P
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
2.8
作者：
Fuscoe, James C.;Tong, Weida;Shi, Leming
通讯作者：
Shi, Leming