Effects of filtering by Present call on analysis of microarray experiments.
Effects of filtering by Present call on analysis of microarray experiments.
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通过当前调用过滤对微阵列实验分析的影响。
DOI：
10.1186/1471-2105-7-49
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发表时间：
2006-01-31
影响因子：
3
通讯作者：
Edenberg, HJ
中科院分区：
文献类型：
--
作者：
McClintick, JN;Edenberg, HJ
Affyscore GeneChips®广泛用于数万个基因的表达谱分析。大量的比较可能导致误报。已经使用了各种方法来减少假阳性，但很少对它们进行比较或定量评估。在这里，我们描述和评估一个简单的方法，使用检测（存在/不存在）调用产生的Affymbre微阵列套件版本5软件（MAS 5），以消除数据，不可靠的检测之前，进一步分析，并比较这与表达水平的过滤。我们探讨了各种阈值的影响，以消除数据在不同规模的实验（从3到10阵列每次治疗），以及他们的相对权力，以检测显着差异的表达。我们的方法为至少一个治疗组中被称为存在的阵列的分数设置了阈值。该方法在执行比较之前移除大部分称为“不存在”的探针组，同时保留大部分称为“存在”的探针组。它优先保留更重要的探针组（p ≤ 0.001）和那些打开或关闭的探针组，并提高了错误发现率。估计假阳性的排列指示由过滤器移除的探针组贡献了不成比例的假阳性数量。当应用于由MAS 5算法或其他探针级算法（例如RMA（鲁棒多芯片平均））生成的数据时，按分数存在过滤是有效的。实验规模极大地影响了可重复检测显著差异的能力，也影响了过滤效果;较小的实验（每个处理组3-5个样本）受益于更严格的过滤（≥50%存在）。使用阈值分数的当前检测调用（由MAS 5导出）提供了一种简单的方法，该方法有效地从分析探针组中消除了不太可能可靠的探针组，同时保留了最重要的探针组和打开或关闭的探针组;从而增加了真阳性与假阳性的比率。
Affymetrix GeneChips® are widely used for expression profiling of tens of thousands of genes. The large number of comparisons can lead to false positives. Various methods have been used to reduce false positives, but they have rarely been compared or quantitatively evaluated. Here we describe and evaluate a simple method that uses the detection (Present/Absent) call generated by the Affymetrix microarray suite version 5 software (MAS5) to remove data that is not reliably detected before further analysis, and compare this with filtering by expression level. We explore the effects of various thresholds for removing data in experiments of different size (from 3 to 10 arrays per treatment), as well as their relative power to detect significant differences in expression. Our approach sets a threshold for the fraction of arrays called Present in at least one treatment group. This method removes a large percentage of probe sets called Absent before carrying out the comparisons, while retaining most of the probe sets called Present. It preferentially retains the more significant probe sets (p ≤ 0.001) and those probe sets that are turned on or off, and improves the false discovery rate. Permutations to estimate false positives indicate that probe sets removed by the filter contribute a disproportionate number of false positives. Filtering by fraction Present is effective when applied to data generated either by the MAS5 algorithm or by other probe-level algorithms, for example RMA (robust multichip average). Experiment size greatly affects the ability to reproducibly detect significant differences, and also impacts the effect of filtering; smaller experiments (3–5 samples per treatment group) benefit from more restrictive filtering (≥50% Present). Use of a threshold fraction of Present detection calls (derived by MAS5) provided a simple method that effectively eliminated from analysis probe sets that are unlikely to be reliable while preserving the most significant probe sets and those turned on or off; it thereby increased the ratio of true positives to false positives.
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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
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
11.5
作者：
Modlich, O;Prisack, HB;Bojar, H
通讯作者：
Bojar, H
影响因子：
4.4
作者：
Perrier, P;Martinez, FO;Mantovani, A
通讯作者：
Mantovani, A
影响因子：
2.3
作者：
Taylor, MW;Grosse, WM;Edenberg, HJ
通讯作者：
Edenberg, HJ