論文題目：Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods
scholar 引用：258
頁數：10
發表時間：February 28, 2011
發表刊物：PLOS ONE
作者：Chao Chen1,2, Kay Grennan2, Judith Badner2, Dandan Zhang3, Elliot Gershon2, Li Jin1, Chunyu Liu
復旦大學
摘要：
The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by ‘‘batch effects,’’ the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
ComBat（2007年提出的方法）是六個方法中效果最好的。
Discussion：
- we now know that the causes of batch effects include variables simply not under the control of the researcher. 批次效應一定存在，很多因素無法控制，再好的實驗設計也沒用。
- In the SMRI brain expression microarray data set, batch effects accounted for nearly 50% of the observed variation in expression, to which site effects contributed 42% and date effects 7.3%.batch effects主要包含site effects和date effects
- 1.ComBat outperformed other methods overall. Combat性能最佳！
- Its parametric and non-parametric algorithms both worked well in both kinds of data sets, controlling the variation attributable to batch effects, increasing the correlation among replicates, and producing the largest AUC in our assessment of overall performance. ComBat_p和ComBat_n性能指標評估都佳！
- We also confirmed another advantage of ComBat: it can robustly manage high-dimensional data when sample sizes are small, which is important for experiments with limited sample size, meta-analyses and clinical diagnostics. 在高維低樣本量的數據集中，combat也是使用，這對於樣本量侷限、薈萃分析和臨牀診斷的情況下都很適用。
- Moreover, ComBat not only worked well on data generated on the Affymetrix platform, but has also been reported to work well with Illumina BeadChips data [28].
- 2.DWD方法的缺點：did not perform well in our analyses when batch sizes were small；it can only analyze two batches at a time. the standardization or normalization in DWD can change the scale between cases and controls；
- 3.SVA方法的缺陷：SVA is based on SVD. 首先，it is not necessarily a simple matter to identify the batch effect eigenvector(特徵向量)，Batch effects may actually contribute substantially to several of the top eigenvectors, so SVD may not identify and remove all the batch effects, and may remove other effects not related to batch. 其次，a basic assumption of SVD is that the eigenvectors have Gaussian distributions. Batch effects, however, may be due to changes in technician, reagents, environmental conditions, scanner effects and/or other variables; this complicated situation may result in batch effects not being distributed in a Gaussian manner. 最後，這個方法的魯棒性不強。
- 4.Ratio_G 有研究表明這個方法outperforms other methods in adjusting data for use in a predictive model, and reasoned that it is because non-ratio- based methods can confound batch and biological effects when one batch has a reverse negative/positive ratio compared to another batch.但是在本研究中，不怎麼滴。accuracy and ROC-AUC results indicated that Ratio_G performed worse than ComBat_p and ComBat_n. Also, Ratio_G performed worst in removing batch effects from the SMRI data.
- 5.PAMR It did very well in our measures of accuracy because of this simple transformation. Again, though, batch effects are compli- cated, and do not affect all samples equally. PAMR does treat all samples equally, so it can over- or under-correct particular samples and came in second to ComBat in our measures of precision. 可以說僅次於ComBat
- 對於不良的實驗設計，再好的方法也沒有用。
- 介紹了一些同行的評估方法工作MAQC-II project
- 討論了本文工作較同行工作的優點。
- we took only date and site effects into consideration, since platform-, channel- or tissue-dependent variations are avoidable with careful experimental design.
- 解釋MAQC-II project中認爲Ratio_G效果最好，而本研究中結論不一致的原因。
- Our evaluation makes clear that adjustment for batch effects is a mandatory step in the analysis of microarray data when the sample size is too large to fit in a single batch.
- ComBat was best able to reduce and remove batch effects while increasing precision and accuracy.
- PAMR was a close second, but its performance suffered when batch size was small; only ComBat performed robustly when adjusting small batches.
Introduction：
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Gene expression microarray technology is Promising
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引出batch effect的定義：the term ‘‘batch’’ refers to microarrays processed at one site over a short period of time using the same platform. 同樣的平臺，同一個地點，短時間內
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The cumulative(累積) error introduced by these time and place-dependent experimental variations is referred to as ‘‘batch effects."
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Batch effects are almost inevitable; largely because most of the available microarray platforms can assay fewer than 24 samples at a time (the latest technology may process 96 samples in each batch). 平臺處理能力有限，不得不分多個batch
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選了六種方法，ComBat分有參數和無參數兩種。
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Distance-weighted discrimination (DWD)[11], based on the Support Vector Ma- chines (SVM) algorithm, is a two-class discrimination analysis for high-dimension low sample size data.
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Mean-centering (PAMR)[12] is a gene-wise one-way analysis of variance (ANOVA).
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Surrogate variable analysis (SVA)[13], combines singular value decomposition (SVD) and a linear model analysis to estimate the eigenvalues from a residual expression matrix from which biological variation has already been removed.
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Geometric ratio-based method (Ratio_G) scales sample measurements by the geometric mean of a group of reference measurements [14].
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An Empirical Bayes method, called Combating Batch Effects When Combining Batches of Gene Expression Microarray Data (ComBat)[15], estimates parameters for location and scale adjustment of each batch for each gene independently[16]; ComBat includes two methods, a parametric prior method (ComBat_p) and a non-parametric method (ComBat_n), based on the prior distributions of the estimated parameters.
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排除了一些方法比如說SVD，Ra- tio_A等是因爲先前的很多研究已經表明了它們不如上述六種方法某一種，或者是上述六種方法某一種的微變形。
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since they are based on different statistical models, their accuracy, precision and overall effectiveness vary.
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用模擬數據預估，因爲知道batch effect的初始值，再用實驗數據進行驗證。
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兩個模擬數據集：
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Variation Assessment Simulated (VAS), comprising 100 samples, 65 of which were assigned to Profile 1 and 35 of which were assigned to Profile 2, where profile was a generic random variable.
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Accuracy Assessment Simulated (AAS) ：consisted of 100 cases and 100 controls, with 1,200 out of 10,000 genes being differentially expressed with 12 different fold change values ranging from 23 to 3.
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實驗數據集：Stanley Medical Research Institute (SMRI) data set ： 62 individuals, with each replicate processed in one of three laboratories [18] Based on place and date of processing, the samples were run in 23 batches, averaging eight samples with at least one case and one control per batch.
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The VAS and SMRI data were used for variation and precision assessment; the AAS and spike-in data were used for accuracy and overall performance evaluation.
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All the batch adjustment methods were applied after experimental data were pre-processed by robust multiarray analysis (RMA) 通用處理
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評估方法：
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We first measured how much each program reduces the variation caused by batch effects.
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To test the programs’ precision, we assessed whether the expression values of the technical replicates correlated better before or after batch adjustment,
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As a measure of accuracy, the programs’ abilities to accurately quantify fold change in expression were assessed using the correlation between nominal fold changes and observed fold changes.
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To assess the overall detection ability of each program, we used a receiver operator characteristic (ROC) curve.
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Our ultimate goal was to identify the batch adjustment method that best prepares data from multiple batches for analysis or meta- analysis to be integrated, as measured by batch effects reduction, accuracy, precision and overall performance.最終，ComBat獲勝，而這個又包含兩種方法，倒是可以分析一下兩種方法的優劣。
正文組織架構：
1. Introduction
2. Results
2.1 Proportion of variation attributable to batch effects
2.2 Precision
2.3 Accuracy
2.4 Overall performance
3. Discussion
4. Materials and Methods
4.1 Samples
4.2 Expression microarray data simulations
4.3 Measuring source of variation
5. Supporting information
正文部分內容摘錄：
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Signal detection slopes were calculated using the spkTools[21] R package.
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The significances of differences between slopes were assessed with a test for homogeneity of slope[32], which was done with the NCStats R package[33].
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The evaluation of overall performance was performed using the ROCR[34] R package from Bioconductor[35].