| Title: | Batch Effect Removal and Addon Normalization (in Phenotype Prediction using Gene Data) |
|---|---|
| Description: | Various tools dealing with batch effects, in particular enabling the removal of discrepancies between training and test sets in prediction scenarios. Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang, 2008) is implemented to enable integrating the quantile normalization step into prediction rules. The following batch effect removal methods are implemented: FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G. For each of these we provide an additional function which enables a posteriori ('addon') batch effect removal in independent batches ('test data'). Here, the (already batch effect adjusted) training data is not altered. For evaluating the success of batch effect adjustment several metrics are provided. Moreover, the package implements a plot for the visualization of batch effects using principal component analysis. The main functions of the package for batch effect adjustment are ba() and baaddon() which enable batch effect removal and addon batch effect removal, respectively, with one of the seven methods mentioned above. Another important function here is bametric() which is a wrapper function for all implemented methods for evaluating the success of batch effect removal. For (addon) quantile normalization and (addon) RMA normalization the functions qunormtrain(), qunormaddon(), rmatrain() and rmaaddon() can be used. |
| Authors: | Roman Hornung, David Causeur |
| Maintainer: | Roman Hornung <[email protected]> |
| License: | GPL-2 |
| Version: | 1.1 |
| Built: | 2024-11-20 05:52:23 UTC |
| Source: | https://github.com/cran/bapred |
This is a short summary of the features of bapred, a package in R for the treatment and analysis of batch effects based in high-dimensional molecular data via batch effect adjustment and addon quantile normalization. Here, a special focus is set on phenotype prediction in the presence of batch effects.
Various tools dealing with batch effects, in particular enabling the
removal of discrepancies between training and test sets in prediction scenarios.
Moreover, addon quantile normalization and addon RMA normalization (Kostka & Spang,
2008) is implemented to enable integrating the quantile normalization step into
prediction rules. The following batch effect removal methods are implemented:
FAbatch, ComBat, (f)SVA, mean-centering, standardization, Ratio-A and Ratio-G.
For each of these we provide an additional function which enables a posteriori
('addon') batch effect removal in independent batches ('test data'). Here, the
(already batch effect adjusted) training data is not altered. For evaluating the
success of batch effect adjustment several metrics are provided. Moreover, the
package implements a plot for the visualization of batch effects using principal
component analysis. The main functions of the package for batch effect adjustment
are ba() and baaddon() which enable batch effect removal and addon batch effect
removal, respectively, with one of the seven methods mentioned above. Another
important function here is bametric() which is a wrapper function for all implemented
methods for evaluating the success of batch effect removal. For (addon) quantile
normalization and (addon) RMA normalization the functions qunormtrain(),
qunormaddon(), rmatrain() and rmaaddon() can be used.
Roman Hornung, David Causeur
Maintainer: Roman Hornung [email protected]
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
# Load example dataset: data(autism) # Subset this example dataset to reduce the # computational burden of the toy examples: # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] # Split into training and test sets: trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) # Batch effect adjustment: combatparams <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, method = "combat") Xsubtraincombat <- combatparams$xadj meancenterparams <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, method = "meancenter") Xsubtrainmeancenter <- meancenterparams$xadj # Addon batch effect adjustment: Xsubtestcombat <- baaddon(params=combatparams, x=Xsubtest, batch=batchsubtest) Xsubtestmeancenter <- baaddon(params=meancenterparams, x=Xsubtest, batch=batchsubtest) # Metrics for evaluating the success of batch effect adjustment: bametric(xba=Xsubtrain, batch=batchsubtrain, metric = "sep") bametric(xba=Xsubtrainmeancenter, batch=batchsubtrain, metric = "sep") bametric(x=Xsubtrain, batch=batchsubtrain, y=ysubtrain, metric = "diffexpr", method = "meancenter") bametric(xba=Xsubtrainmeancenter, x=Xsubtrain, metric = "cor") # Principal component analysis plots for the visualization # of batch effects: par(mfrow=c(1,3)) pcplot(x=Xsub, batch=batchsub, y=ysub, alpha=0.25, main="alpha = 0.25") pcplot(x=Xsub, batch=batchsub, y=ysub, alpha=0.75, main="alpha = 0.75") pcplot(x=Xsub, batch=batchsub, y=ysub, col=1:length(unique(batchsub)), main="col = 1:length(unique(batchsub))") par(mfrow=c(1,1)) # (Addon) quantile normalization: qunormparams <- qunormtrain(x=Xsubtrain) Xtrainnorm <- qunormparams$xnorm Xtestaddonnorm <- qunormaddon(qunormparams, x=Xsubtest)# Load example dataset: data(autism) # Subset this example dataset to reduce the # computational burden of the toy examples: # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] # Split into training and test sets: trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) # Batch effect adjustment: combatparams <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, method = "combat") Xsubtraincombat <- combatparams$xadj meancenterparams <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, method = "meancenter") Xsubtrainmeancenter <- meancenterparams$xadj # Addon batch effect adjustment: Xsubtestcombat <- baaddon(params=combatparams, x=Xsubtest, batch=batchsubtest) Xsubtestmeancenter <- baaddon(params=meancenterparams, x=Xsubtest, batch=batchsubtest) # Metrics for evaluating the success of batch effect adjustment: bametric(xba=Xsubtrain, batch=batchsubtrain, metric = "sep") bametric(xba=Xsubtrainmeancenter, batch=batchsubtrain, metric = "sep") bametric(x=Xsubtrain, batch=batchsubtrain, y=ysubtrain, metric = "diffexpr", method = "meancenter") bametric(xba=Xsubtrainmeancenter, x=Xsubtrain, metric = "cor") # Principal component analysis plots for the visualization # of batch effects: par(mfrow=c(1,3)) pcplot(x=Xsub, batch=batchsub, y=ysub, alpha=0.25, main="alpha = 0.25") pcplot(x=Xsub, batch=batchsub, y=ysub, alpha=0.75, main="alpha = 0.75") pcplot(x=Xsub, batch=batchsub, y=ysub, col=1:length(unique(batchsub)), main="col = 1:length(unique(batchsub))") par(mfrow=c(1,1)) # (Addon) quantile normalization: qunormparams <- qunormtrain(x=Xsubtrain) Xtrainnorm <- qunormparams$xnorm Xtestaddonnorm <- qunormaddon(qunormparams, x=Xsubtest)
Total RNA obtained from lmyphoblast cell lines derived from 250 individuals, 137 of which suffer from autism and 113 are healthy. The dataset consists of four batches of sizes 101, 96, 45 and 8.
data(autism)data(autism)
1) X - the covariate matrix: a matrix of dimension 250 x 1000, containing the numerical transcript values
2) batch - the batch variable: a factor with levels '1', '2', '3' and '4'
3) y - the target variable: a factor with levels '1' corresponding to 'healthy' and '2' corresponding to 'autism'
The RNA measurements were obtained by the Illumina HumanRef-8 v3.0 Expression BeadChip featuring 24,526 transcripts. To reduce computational burden of potential analyses performed using this dataset we randomly selected 1,000 of these 24,526 transcripts. Moreover, the original dataset consisted of five batches and contained measurements of 439 individuals. Again to reduce computational burden of potential analyses we excluded the biggest batch featuring 189 individuals resulting in the 250 individuals included in the dataset made available in bapred.
ArrayExpress, accession number: E-GEOD-37772
Luo, R., Sanders, S. J., Tian, Y., Voineagu, I., Huang, N., Chu, S. H., Klei, L., Cai, C., Ou, J., Lowe, J. K., Hurles, M. E., Devlin, B., State, M. W., Geschwind, D. H. (2012). Genome-wide Transcriptome Profiling Reveals the Functional Impact of Rare De Novo and Recurrent CNVs in Autism Spectrum Disorders. The American Journal of Human Genetics 91:38–55, <doi:10.1016/j.ajhg.2012.05.011>.
data(autism)data(autism)
This metric is concerned with the minimal distances between pairs of batches.
avedist(xba, batch)avedist(xba, batch)
xba |
matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
For two batches j and j* (see next paragraph for the case with more batches): 1) for each observation in batch j the euclidean distance to the nearest observation in batch j* is calculated; 2) the roles of j and j* are switched and 1) is re-performed; 3) the average is taken over all n_j + n_j* minimal distances.
For more than two batches: 1) for all possible pairs of batches: calculate the metric as described above; 2) calculate the weighted average of the values in 1) with weights proportional to the sum of the sample sizes in the two respective batches.
The variables are standardized before the calculation to make the metric independent of scale.
Value of the metric
The smaller the values of this metric, the better.
Roman Hornung
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter,C., Weiss-Solís, D. Y., Duque, R., Bersini, H., Nowé, A. (2012). Batch effect removal methods for microarray gene expression data integration: a survey. Briefings in Bioinformatics 14(4):469-490, <doi:10.1093/bib/bbs037>.
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) avedist(xba=X, batch=batch)data(autism) avedist(xba=X, batch=batch)
Performs batch effect adjustment using one of the following methods: FAbatch, ComBat, SVA, mean-centering, standardization, Ratio-A, Ratio-G or "no batch effect adjustment". Additionally returns information necessary for addon batch effect adjustment with the respective method. The latter can be done using baaddon.
ba(x, y, batch, method = c("fabatch", "combat", "sva", "meancenter", "standardize", "ratioa", "ratiog", "none"), ...)ba(x, y, batch, method = c("fabatch", "combat", "sva", "meancenter", "standardize", "ratioa", "ratiog", "none"), ...)
x |
matrix. The covariate matrix. observations in rows, variables in columns. |
y |
factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. Only used for |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
method |
character. Batch effect adjustment method. |
... |
additional arguments to be passed to |
This function is merely for convenience - a wrapper function for fabatch, combatba, svaba, meancenter, standardize, ratioa, ratiog and noba.
The output of fabatch, combatba, svaba, meancenter, standardize, ratioa, ratiog or noba respectively.
The following methods are NOT recommended in cross-study prediction settings: FAbatch (fabatch), frozen SVA (svaba), standardization (standardize) as well as no addon batch effect adjustment (noba).
Given a not too small test set, the following methods are recommended (Hornung et al., 2016b): ComBat (combatba), mean-centering (meancenter), Ratio-A (ratioa), Ratio-G (ratiog).
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
Johnson, W. E., Rabinovic, A., Li, C. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8:118-127, <doi:10.1093/biostatistics/kxj037>.
Leek, J. T., Storey, J. D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics 3:1724-1735, <doi:10.1371/journal.pgen.0030161>.
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
Parker, H. S., Bravo, H. C., Leek, J. T. (2014). Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2:e561, <doi:10.7717/peerj.561>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] somemethods <- c("fabatch", "combat", "meancenter", "none") adjusteddata <- list() for(i in seq(along=somemethods)) { cat(paste("Adjusting using method = \"", somemethods[i], "\"", sep=""), "\n") adjusteddata[[i]] <- ba(x=Xsub, y=ysub, batch=batchsub, method = somemethods[i])$xadj }data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] somemethods <- c("fabatch", "combat", "meancenter", "none") adjusteddata <- list() for(i in seq(along=somemethods)) { cat(paste("Adjusting using method = \"", somemethods[i], "\"", sep=""), "\n") adjusteddata[[i]] <- ba(x=Xsub, y=ysub, batch=batchsub, method = somemethods[i])$xadj }
Performs addon batch effect adjustment for a method of choice: takes the output of ba or that of one of the functions performing a specific batch effect adjustment method (e.g. fabatch or svaba) and new batch data. Then performs the respective batch effect adjustment method on the new batch data.
baaddon(params, x, batch)baaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
The following methods are NOT recommended in cross-study prediction settings: FAbatch (fabatch), frozen SVA (svaba), standardization (standardize) as well as no addon batch effect adjustment (noba).
Given a not too small test set, the following methods are recommended (Hornung et al., 2016b): ComBat (combatba), mean-centering (meancenter), Ratio-A (ratioa), Ratio-G (ratiog).
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
Johnson, W. E., Rabinovic, A., Li, C. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8:118-127, <doi:10.1093/biostatistics/kxj037>.
Leek, J. T., Storey, J. D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics 3:1724-1735, <doi:10.1371/journal.pgen.0030161>.
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
Parker, H. S., Bravo, H. C., Leek, J. T. (2014). Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2:e561, <doi:10.7717/peerj.561>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) somemethods <- c("fabatch", "combat", "meancenter", "none") adjustedtestdata <- list() for(i in seq(along=somemethods)) { cat(paste("Adjusting training data using method = \"", somemethods[i], "\"", sep=""), "\n") paramstemp <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, method = somemethods[i]) cat(paste("Addon adjusting test data using method = \"", somemethods[i], "\"", sep=""), "\n") adjustedtestdata[[i]] <- baaddon(params=paramstemp, x=Xsubtest, batch=batchsubtest) }data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) somemethods <- c("fabatch", "combat", "meancenter", "none") adjustedtestdata <- list() for(i in seq(along=somemethods)) { cat(paste("Adjusting training data using method = \"", somemethods[i], "\"", sep=""), "\n") paramstemp <- ba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain, method = somemethods[i]) cat(paste("Addon adjusting test data using method = \"", somemethods[i], "\"", sep=""), "\n") adjustedtestdata[[i]] <- baaddon(params=paramstemp, x=Xsubtest, batch=batchsubtest) }
Diverse metrics measuring the severeness of batch effects in (batch effect adjusted) data or the performance of certain analyses performed using the latter. This is a wrapper function for the following functions, where each of them calculates a certain metric: sepscore, avedist, kldist, skewdiv, pvcam, diffexprm and corba. For details see the documentation of the latter.
bametric(xba, batch, y, x, metric = c("sep", "avedist", "kldist", "skew", "pvca", "diffexpr", "cor"), method, ...)bametric(xba, batch, y, x, metric = c("sep", "avedist", "kldist", "skew", "pvca", "diffexpr", "cor"), method, ...)
xba |
matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
y |
factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. Only used for |
x |
matrix. The covariate matrix before batch effect adjustment. observations in rows, variables in columns. |
metric |
character. The metric to use. |
method |
character. The batch effect adjustment method to use for |
... |
additional arguments to be passed to |
Value of the respective metric
Roman Hornung
Boltz, S., Debreuve, E., Barlaud, M. (2009). High-dimensional statistical measure for region-of-interest tracking. Transactions in Image Processing 18(6):1266-1283, <doi:10.1109/TIP.2009.2015158>.
Geyer, C. J., Meeden, G., D. (2005). Fuzzy and randomized confidence intervals and p-values (with discussion). Statistical Science 20(4):358-387, <doi:10.1214/088342305000000340>.
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter,C., Weiss-Solís, D. Y., Duque, R., Bersini, H., Nowé, A. (2012). Batch effect removal methods for microarray gene expression data integration: a survey. Briefings in Bioinformatics 14(4):469-490, <doi:10.1093/bib/bbs037>.
Lee, J. A., Dobbin, K. K., Ahn, J. (2014). Covariance adjustment for batch effect in gene expression data. Statistics in Medicine 33:2681-2695, <doi:10.1002/sim.6157>.
Li, J., Bushel, P., Chu, T.-M., Wolfinger, R.D. (2009). Principal variance components analysis: Estimating batch effects in microarray gene expression data. In: Scherer, A. (ed) Batch Effects and Noise in Microarray Experiments: Sources and Solutions, John Wiley & Sons, Chichester, UK, <doi:10.1002/9780470685983.ch12>.
Shabalin, A. A., Tjelmeland, H., Fan, C., Perou, C. M., Nobel, A. B. (2008). Merging two gene-expression studies via cross-platform normalization. Bioinformatics 24(9):1154-1160, <doi:10.1093/bioinformatics/btn083>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] Xsubadj <- ba(x=Xsub, y=ysub, batch=batchsub, method = "combat")$xadj bametric(xba=Xsub, batch=batchsub, metric = "sep") bametric(xba=Xsubadj, batch=batchsub, metric = "sep") bametric(xba=Xsub, batch=batchsub, metric = "avedist") bametric(xba=Xsubadj, batch=batchsub, metric = "avedist") bametric(xba=Xsub, batch=batchsub, metric = "kldist") bametric(xba=Xsubadj, batch=batchsub, metric = "kldist") bametric(xba=Xsub, batch=batchsub, metric = "skew") bametric(xba=Xsubadj, batch=batchsub, metric = "skew") bametric(xba=Xsub, batch=batchsub, y=ysub, metric = "pvca") bametric(xba=Xsubadj, batch=batchsub, y=ysub, metric = "pvca") bametric(x=Xsub, batch=batchsub, y=ysub, metric = "diffexpr", method = "combat") bametric(xba=Xsubadj, x=Xsub, metric = "cor")data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] Xsubadj <- ba(x=Xsub, y=ysub, batch=batchsub, method = "combat")$xadj bametric(xba=Xsub, batch=batchsub, metric = "sep") bametric(xba=Xsubadj, batch=batchsub, metric = "sep") bametric(xba=Xsub, batch=batchsub, metric = "avedist") bametric(xba=Xsubadj, batch=batchsub, metric = "avedist") bametric(xba=Xsub, batch=batchsub, metric = "kldist") bametric(xba=Xsubadj, batch=batchsub, metric = "kldist") bametric(xba=Xsub, batch=batchsub, metric = "skew") bametric(xba=Xsubadj, batch=batchsub, metric = "skew") bametric(xba=Xsub, batch=batchsub, y=ysub, metric = "pvca") bametric(xba=Xsubadj, batch=batchsub, y=ysub, metric = "pvca") bametric(x=Xsub, batch=batchsub, y=ysub, metric = "diffexpr", method = "combat") bametric(xba=Xsubadj, x=Xsub, metric = "cor")
Performs batch effect adjustment using the parametric version of ComBat and additionally returns information necessary for addon batch effect adjustment with ComBat.
combatba(x, batch)combatba(x, batch)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
combatba returns an object of class combat.
An object of class "combat" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
meanoverall |
vector containing the overall means of the variables. Used in addon adjustment. |
var.pooled |
vector containing the pooled variances of the variables. Used in addon adjustment. |
batch |
batch variable |
nbatches |
number of batches |
The original ComBat-code is used in combatba:
http://www.bu.edu/jlab/wp-assets/ComBat/Download.html (Access date: 2015/06/19)
Roman Hornung
Johnson, W. E., Rabinovic, A., Li, C. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8:118-127, <doi:10.1093/biostatistics/kxj037>.
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
data(autism) combatba(x=X, batch=batch)data(autism) combatba(x=X, batch=batch)
Performs addon batch effect adjustment using ComBat. Takes the output of performing combatba on a training data set and new batch data and correspondingly adjusts the test data to better match the adjusted training data according to the ComBat model
combatbaaddon(params, x, batch)combatbaaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
The original ComBat-code is used in combatbaaddon:
http://www.bu.edu/jlab/wp-assets/ComBat/Download.html (Access date: 2015/06/19)
Roman Hornung
Johnson, W. E., Rabinovic, A., Li, C. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8:118-127, <doi:10.1093/biostatistics/kxj037>.
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- combatba(x=Xtrain, batch=batchtrain) Xtestaddon <- combatbaaddon(params, x=Xtest, batch=batchtest)data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- combatba(x=Xtrain, batch=batchtrain) Xtestaddon <- combatbaaddon(params, x=Xtest, batch=batchtest)
For each variable Pearson's correlation of the values before and after batch effect adjustment is calculated. Then the mean is taken over all these correlations.
corba(xba, x)corba(xba, x)
xba |
matrix. The covariate matrix after batch effect adjustment. observations in rows, variables in columns. |
x |
matrix. The covariate matrix before batch effect adjustment. observations in rows, variables in columns. |
Value of the metric
Roman Hornung
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter,C., Weiss-Solís, D. Y., Duque, R., Bersini, H., Nowé, A. (2012). Batch effect removal methods for microarray gene expression data integration: a survey. Briefings in Bioinformatics 14(4):469-490, <doi:10.1093/bib/bbs037>.
data(autism) params <- ba(x=X, y=y, batch=batch, method = "combat") Xadj <- params$xadj corba(xba=Xadj, x=X)data(autism) params <- ba(x=X, y=y, batch=batch, method = "combat") Xadj <- params$xadj corba(xba=Xadj, x=X)
This metric is similar to the idea presented in Lazar et al (2012) which consists
in comparing the list of the most differentially expressed genes obtained using a batch
effect adjusted dataset to the list obtained using an independent dataset. For each
batch the following is done by diffexprm: 1) the respective batch is left out
and batch effect adjustment
is performed using the remaining batches; 2) differential expression analysis is performed
once using the left-out batch and once using the remaining batch-effect adjusted data;
3) the overlap between the two lists of genes found differentially expressed in the
two subsets is measured. See below for further details.
diffexprm(x, batch, y, method = c("fabatch", "combat", "sva", "meancenter", "standardize", "ratioa", "ratiog", "none"))diffexprm(x, batch, y, method = c("fabatch", "combat", "sva", "meancenter", "standardize", "ratioa", "ratiog", "none"))
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
y |
factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. |
method |
character. Method for batch effect adjustment. The following are supported: |
The following procedure is performed: 1) For each batch j leave this batch out and perform batch effect adjustment on the rest of the dataset. Derive two lists of the 5 percent of variables which are most differentially expressed (see next paragraph): one using the batch effect adjusted dataset - where batch j was left out - and one using the data from batch j. Calculate the number of variables appearing in both lists and divide this number by the common length of the lists. 2) Calculate a weighted average of the values obtained in 1) with weights proportional to the number of observations in the corresponding left-out batches.
Differential expression is measured as follows. For each variable a randomized p-value out of the Whitney-Wilcoxon rank sum test is drawn, see Geyer and Meeden (2005) for details. Then those 5 percent variables are considered differentially expressed, which are associated with the smallest p-values.
Value of the metric
The larger the values of this metric, the better.
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter,C., Weiss-Solís, D. Y., Duque, R., Bersini, H., Nowé, A. (2012). Batch effect removal methods for microarray gene expression data integration: a survey. Briefings in Bioinformatics 14(4):469-490, <doi:10.1093/bib/bbs037>.
Geyer, C. J., Meeden, G., D. (2005). Fuzzy and randomized confidence intervals and p-values (with discussion). Statistical Science 20(4):358-387, <doi:10.1214/088342305000000340>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] diffexprm(x=Xsub, batch=batchsub, y=ysub, method = "ratiog") diffexprm(x=Xsub, batch=batchsub, y=ysub, method = "none")data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] diffexprm(x=Xsub, batch=batchsub, y=ysub, method = "ratiog") diffexprm(x=Xsub, batch=batchsub, y=ysub, method = "none")
Performs batch effect adjustment using the FAbatch-method described in Hornung et al. (2016) and additionally returns information necessary for addon batch effect adjustment with FAbatch.
fabatch(x, y, batch, nbf = NULL, minerr = 1e-06, probcrossbatch = TRUE, maxiter = 100, maxnbf = 12)fabatch(x, y, batch, nbf = NULL, minerr = 1e-06, probcrossbatch = TRUE, maxiter = 100, maxnbf = 12)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
y |
factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
nbf |
integer. Number of factors to estimate in all batches. If not given the number of factors is estimated automatically for each batch. Recommended to leave unspecified. |
minerr |
numeric. Maximal mean quadratic deviations between the estimated residual variances from two consecutive iterations. The iteration stops when this value is undercut. |
probcrossbatch |
logical. Default is |
maxiter |
integer. Maximal number of iterations in the estimation of the latent factors by Maximum Likelihood. |
maxnbf |
integer. Maximal number of factors if |
fabatch returns an object of class fabatch.
An object of class "fabatch" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
m1 |
means of the standardized variables in class '1' |
m2 |
means of the standardized variables in class '2' |
b0 |
intercept out of the L2-penalized logistic regression performed for estimation of the class probabilities |
b |
variable coefficients out of the L2-penalized logistic regression performed for estimation of the class probabilities |
pooledsds |
vector containing the pooled standard deviations of the variables |
meanoverall |
vector containing the variable means |
minerr |
maximal mean quadratic deviations between the estimated residual variances from two consecutive iterations |
nbfinput |
user-specified number of latent factors |
badvariables |
indices of those variables which are constant in at least one batch |
nbatches |
number of batches |
batch |
batch variable |
nbfvec |
vector containing the numbers of factors in the individual batches |
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] fabatch(x=Xsub, y=ysub, batch=batchsub)data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] fabatch(x=Xsub, y=ysub, batch=batchsub)
Performs addon batch effect adjustment using FAbatch. Takes the output of performing fabatch on a training data set and new batch data and correspondingly adjusts the test data to better match the adjusted training data according to the FAbatch model.
fabatchaddon(params, x, batch)fabatchaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
It is not recommended to perform FAbatch in cross-study prediction settings, because it has been observed to (strongly) impair prediction performance. Given a not too small test set, the following methods are recommended (Hornung et al., 2016b): combatba, meancenter, ratioa, ratiog.
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) params <- fabatch(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain) Xsubtestaddon <- fabatchaddon(params, x=Xsubtest, batch=batchsubtest)data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) params <- fabatch(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain) Xsubtestaddon <- fabatchaddon(params, x=Xsubtest, batch=batchsubtest)
This metric estimates the Kullback-Leibler divergences between the distributions of the within and that of the between batch euclidian distances of pairs of observations. These distributions should be similar in the abscence of stronger batch effects.
kldist(xba, batch)kldist(xba, batch)
xba |
matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
For two batches j and j* (see next paragraph for the case with more batches): 1) the distances between all pairs of observations in batch j - denoted as {dist_j} - and the distances between all such pairs in batch j* - denoted as {dist_j*} - are calculated; 2) for each observation in j the distances to all observations in j* are calculated, resulting in n_j x n_j* distances denoted as {dist_jj*}; calculate the Kullback-Leibler divergence between the densities of {dist_j} and {dist_jj*} and that between the densities of {dist_j*} and {dist_jj*} - using the k-nearest neighbours based method by Boltz et al (2009) with k=5; 3) take the weighted mean of the values of these two divergences with weights proportional to n_j and n_j*.
For more than two batches: 1) for all possible pairs of batches: calculate the metric as described above; 2) calculate the weighted average of the values in 1) with weights proportional to the sum of the sample sizes in the two respective batches.
The variables are standardized before the calculation to make the metric independent of scale.
Value of the metric
The smaller the values of this metric, the better.
Roman Hornung
Lee, J. A., Dobbin, K. K., Ahn, J. (2014). Covariance adjustment for batch effect in gene expression data. Statistics in Medicine 33:2681-2695, <doi:10.1002/sim.6157>.
Boltz, S., Debreuve, E., Barlaud, M. (2009). High-dimensional statistical measure for region-of-interest tracking. Transactions in Image Processing 18(6):1266-1283, <doi:10.1109/TIP.2009.2015158>.
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) kldist(xba=X, batch=batch)data(autism) kldist(xba=X, batch=batch)
Performs batch effect adjustment by centering the variables within batches to have zero mean.
meancenter(x, batch)meancenter(x, batch)
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
meancenter returns an object of class meancenter.
An object of class "meancenter" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
nbatches |
number of batches |
batch |
batch variable |
Roman Hornung
data(autism) params <- meancenter(x=X, batch=batch)data(autism) params <- meancenter(x=X, batch=batch)
Performs addon batch effect adjustment for mean-centering:
1) takes the output of meancenter applied to a training
data set together with new batch data;
2) checks whether the training data was also adjusted using mean-centering and whether the same number of variables is present in training and new data;
3) performs mean-centering on the new batch data.
meancenteraddon(params, x, batch)meancenteraddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
Because mean-centering is performed "batch by batch" the "addon procedure" for mean-centering consists of plain mean-centering on the new test batches.
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- meancenter(x=Xtrain, batch=batchtrain) Xtestaddon <- meancenteraddon(params=params, x=Xtest, batch=batchtest)data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- meancenter(x=Xtrain, batch=batchtrain) Xtestaddon <- meancenteraddon(params=params, x=Xtest, batch=batchtest)
This function is merely included for consistency. It returns the raw dataset not adjusted for batch effects.
noba(x, batch)noba(x, batch)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
noba returns an object of class noba.
An object of class "noba" is a list containing the following components:
xadj |
matrix of (training) data |
nbatches |
number of batches |
batch |
batch variable |
Roman Hornung
data(autism) Xadj <- noba(x=X, batch=batch)$adj all(as.vector(Xadj)==as.vector(X))data(autism) Xadj <- noba(x=X, batch=batch)$adj all(as.vector(Xadj)==as.vector(X))
This function is merely included for consistency. It does the following:
1) takes the output of noba applied to a training
data set together with new batch data;
2) checks whether the training data has also not been adjusted using a batch effect adjustment method and whether the same number of variables is present in training and new data;
3) returns the new batch data not adjusted for batch effects.
nobaaddon(params, x, batch)nobaaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The unadjusted covariate matrix x of the test data.
It is not recommended to perform no addon batch effect adjustment in cross-study prediction settings. Given a not too small test set, the following methods are recommended (Hornung et al., 2016): combatba, meancenter, ratioa, ratiog.
Roman Hornung
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- noba(x=Xtrain, batch=batchtrain) Xtestaddon <- nobaaddon(params=params, x=Xtest, batch=batchtest) all(as.vector(Xtestaddon)==as.vector(Xtest))data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- noba(x=Xtrain, batch=batchtrain) Xtestaddon <- nobaaddon(params=params, x=Xtest, batch=batchtest) all(as.vector(Xtestaddon)==as.vector(Xtest))
This function performs principal component analysis on the covariate matrix and plots the first two principal components against each other. Different batches are distinguished by different colors and (optionally) the two classes of the target variable by different plot symbols.
pcplot(x, batch, y, alpha = 0.35, ...)pcplot(x, batch, y, alpha = 0.35, ...)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
y |
optional factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. |
alpha |
optional numeric between 0 and 1. Alpha transparency of the contour lines of the batch-specific two-dimensional density estimates. Only applicable when default color scheme ( |
... |
additional arguments to be passed to |
For the data corresponding to each batch a two-dimensional kernel density estimate is obtained using the function kde2d() from the MASS-package. These estimates are depicted through contour lines (using contour).
NULL
Roman Hornung
data(autism) par(mfrow=c(1,3)) pcplot(x=X, batch=batch, y=y, alpha=0.25, main="alpha = 0.25") pcplot(x=X, batch=batch, y=y, alpha=0.75, main="alpha = 0.75") pcplot(x=X, batch=batch, y=y, col=1:length(unique(batch)), main="col = 1:length(unique(batch))") par(mfrow=c(1,1))data(autism) par(mfrow=c(1,3)) pcplot(x=X, batch=batch, y=y, alpha=0.25, main="alpha = 0.25") pcplot(x=X, batch=batch, y=y, alpha=0.75, main="alpha = 0.75") pcplot(x=X, batch=batch, y=y, col=1:length(unique(batch)), main="col = 1:length(unique(batch))") par(mfrow=c(1,1))
Principal Variance Component Analysis (PVCA) (Li et al, 2009) allows the estimation of the contribution of several sources of variability. pvcam uses it to estimate the proportion of variance in the data explained by the class signal. See below for a more detailed explanation of what the function does.
pvcam(xba, batch, y, threshold = 0.6)pvcam(xba, batch, y, threshold = 0.6)
xba |
matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
y |
factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. |
threshold |
numeric. Minimal proportion of variance explained by the principal components used. |
In PVCA, first principal component analysis is performed on the n x n covariance matrix between the
observations. Then, using a random effects model
the principal components are regressed on arbitrary factors of variability, such
as "batch" and "(phenotype) class". Ultimately, estimated proportions of variance induced by each factor and that of the residual variance are obtained.
In pvcam the factors included into the model are: "batch", "class" and the interaction of these two into.
The metric calculated by pvcam is the proportion of variance
explained by "class".
pvcam uses a slightly altered version of the function pvcaBatchAssess() from the Bioconductor package pvca.
The latter was altered to take the covariate data as a matrix instead of as an object of class ExpressionSet.
Value of the metric
Higher values of this metric indicate a better preservation or exposure, respectively, of the biological signal of interest.
Roman Hornung
Li, J., Bushel, P., Chu, T.-M., Wolfinger, R.D. (2009). Principal variance components analysis: Estimating batch effects in microarray gene expression data. In: Scherer, A. (ed) Batch Effects and Noise in Microarray Experiments: Sources and Solutions, John Wiley & Sons, Chichester, UK, <doi:10.1002/9780470685983.ch12>.
data(autism) Xadj <- ba(x=X, y=y, batch=batch, method = "combat")$xadj pvcam(xba = X, batch = batch, y = y) pvcam(xba = Xadj, batch = batch, y = y)data(autism) Xadj <- ba(x=X, y=y, batch=batch, method = "combat")$xadj pvcam(xba = X, batch = batch, y = y) pvcam(xba = Xadj, batch = batch, y = y)
Performs addon quantile normalization by using documentation by value (Kostka & Spang, 2008).
qunormaddon(params, x)qunormaddon(params, x)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
This function uses code from the off-CRAN package docval, version 1.0.
The covariate matrix of the test data after addon quantile normalization. Observations in rows, variables in columns.
Roman Hornung
Kostka, D., Spang, R. (2008). Microarray based diagnosis profits from better documentation of gene expression signatures. PLoS Computational Biology 4(2):e22, <doi:10.1371/journal.pcbi.0040022>.
data(autism) Xtrain <- X[batch==1,] Xtest <- X[batch==2,] params <- qunormtrain(x=Xtrain) Xtrainnorm <- params$xnorm Xtestaddonnorm <- qunormaddon(params, x=Xtest)data(autism) Xtrain <- X[batch==1,] Xtest <- X[batch==2,] params <- qunormtrain(x=Xtrain) Xtrainnorm <- params$xnorm Xtestaddonnorm <- qunormaddon(params, x=Xtest)
Performs quantile normalization for a covariate matrix and returns the normalized dataset together with information necessary for addon quantile normalization (Kostka & Spang, 2008) using qunormaddon.
qunormtrain(x)qunormtrain(x)
x |
matrix. The covariate matrix. observations in rows, variables in columns. |
This function uses code from the off-CRAN package docval, version 1.0.
qunormtrain returns an object of class qunormtrain.
An object of class "qunormtrain" is a list containing the following components:
xnorm |
matrix of quantile normalized (training) data. Observations in rows, variables in columns. |
mqnts |
Vector of length ncol(xnorm). Averages of sorted values, with averages taken across observations. |
Roman Hornung
Kostka, D., Spang, R. (2008). Microarray based diagnosis profits from better documentation of gene expression signatures. PLoS Computational Biology 4(2):e22, <doi:10.1371/journal.pcbi.0040022>.
data(autism) Xtrain <- X[batch==1,] params <- qunormtrain(x=Xtrain) Xtrainnorm <- params$xnormdata(autism) Xtrain <- X[batch==1,] params <- qunormtrain(x=Xtrain) Xtrainnorm <- params$xnorm
Performs batch effect adjustment using Ratio-A. Here, the variable values are divided by the batch-specific arithmetic mean of the corresponding variable.
ratioa(x, batch)ratioa(x, batch)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
ratioa returns an object of class ratioa.
An object of class "ratioa" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
nbatches |
number of batches |
batch |
batch variable |
Roman Hornung
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
data(autism) params <- ratioa(x=X, batch=batch)data(autism) params <- ratioa(x=X, batch=batch)
Performs addon batch effect adjustment for Ratio-A:
1) takes the output of ratioa applied to a training
data set together with new batch data;
2) checks whether the training data was also adjusted using Ratio-A and whether the same number of variables is present in training and new data;
3) performs Ratio-A on the new batch data.
ratioaaddon(params, x, batch)ratioaaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
Because Ratio-A is performed "batch by batch" the "addon procedure" for Ratio-A consists of plain Ratio-A on the new test batches.
Roman Hornung
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- ratioa(x=Xtrain, batch=batchtrain) Xtestaddon <- ratioaaddon(params=params, x=Xtest, batch=batchtest)data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- ratioa(x=Xtrain, batch=batchtrain) Xtestaddon <- ratioaaddon(params=params, x=Xtest, batch=batchtest)
Performs batch effect adjustment using Ratio-G. Here, the variable values are divided by the batch-specific geometric mean of the corresponding variable.
ratiog(x, batch)ratiog(x, batch)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
ratiog returns an object of class ratiog.
An object of class "ratiog" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
nbatches |
number of batches |
batch |
batch variable |
Roman Hornung
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
data(autism) params <- ratiog(x=X, batch=batch)data(autism) params <- ratiog(x=X, batch=batch)
Performs addon batch effect adjustment for Ratio-G:
1) takes the output of ratiog applied to a training
data set together with new batch data;
2) checks whether the training data was also adjusted using Ratio-G and whether the same number of variables is present in training and new data;
3) performs Ratio-G on the new batch data.
ratiogaddon(params, x, batch)ratiogaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
Because Ratio-G is performed "batch by batch" the "addon procedure" for Ratio-G consists of plain Ratio-G on the new test batches.
Roman Hornung
Luo, J., Schumacher, M., Scherer, A., Sanoudou, D., Megherbi, D., Davison, T., Shi, T., Tong, W., Shi, L., Hong, H., Zhao, C., Elloumi, F., Shi, W., Thomas, R., Lin, S., Tillinghast, G., Liu, G., Zhou, Y., Herman, D., Li, Y., Deng, Y., Fang, H., Bushel, P., Woods, M., Zhang, J. (2010). A comparison of batch effect removal methods for enhancement of prediction performance using maqc-ii microarray gene expression data. The Pharmacogenomics Journal 10:278-291, <doi:10.1038/tpj.2010.57>.
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- ratiog(x=Xtrain, batch=batchtrain) Xtestaddon <- ratiogaddon(params=params, x=Xtest, batch=batchtest)data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- ratiog(x=Xtrain, batch=batchtrain) Xtestaddon <- ratiogaddon(params=params, x=Xtest, batch=batchtest)
Performs RMA with addon quantile normalization by using documentation by value (Kostka & Spang, 2008).
rmaaddon(params, affybatchtest)rmaaddon(params, affybatchtest)
params |
object of class |
affybatchtest |
object of class |
This function uses code from the off-CRAN package docval, version 1.0.
The covariate matrix of the test data after addon normalization. Observations in rows, variables in columns.
Roman Hornung
Kostka, D., Spang, R. (2008). Microarray based diagnosis profits from better documentation of gene expression signatures. PLoS Computational Biology 4(2):e22, <doi:10.1371/journal.pcbi.0040022>.
## Not run: # Read in example data from ArrayExpress-webpage: library("ArrayExpress") expFiles <- getAE("E-GEOD-62837", path = tempdir(), type = "raw") rawfiles <- file.path(tempdir(), expFiles$rawFiles) library("affy") # Training data: affybatchtrain <- ReadAffy(filenames=rawfiles[1:3]) # Test data: affybatchtest <- ReadAffy(filenames=rawfiles[4:5]) try(file.remove(file.path(tempdir(), expFiles$rawFiles))) try(file.remove(file.path(tempdir(), expFiles$processedFiles))) try(file.remove(file.path(tempdir(), expFiles$sdrf))) try(file.remove(file.path(tempdir(), expFiles$idf))) try(file.remove(file.path(tempdir(), expFiles$adf))) try(file.remove(file.path(tempdir(), expFiles$rawArchive))) try(file.remove(file.path(tempdir(), expFiles$processedArchive))) # RMA normalization with documentation by value: rmaparams <- rmatrain(affybatchtrain) Xtrainnorm <- rmaparams$xnorm dim(Xtrainnorm) # RMA addon normalization: Xtestaddonnorm <- rmaaddon(rmaparams, affybatchtest) dim(Xtestaddonnorm) ## End(Not run)## Not run: # Read in example data from ArrayExpress-webpage: library("ArrayExpress") expFiles <- getAE("E-GEOD-62837", path = tempdir(), type = "raw") rawfiles <- file.path(tempdir(), expFiles$rawFiles) library("affy") # Training data: affybatchtrain <- ReadAffy(filenames=rawfiles[1:3]) # Test data: affybatchtest <- ReadAffy(filenames=rawfiles[4:5]) try(file.remove(file.path(tempdir(), expFiles$rawFiles))) try(file.remove(file.path(tempdir(), expFiles$processedFiles))) try(file.remove(file.path(tempdir(), expFiles$sdrf))) try(file.remove(file.path(tempdir(), expFiles$idf))) try(file.remove(file.path(tempdir(), expFiles$adf))) try(file.remove(file.path(tempdir(), expFiles$rawArchive))) try(file.remove(file.path(tempdir(), expFiles$processedArchive))) # RMA normalization with documentation by value: rmaparams <- rmatrain(affybatchtrain) Xtrainnorm <- rmaparams$xnorm dim(Xtrainnorm) # RMA addon normalization: Xtestaddonnorm <- rmaaddon(rmaparams, affybatchtest) dim(Xtestaddonnorm) ## End(Not run)
Performs RMA normalization and returns the normalized dataset together with information necessary for addon RMA normalization (Kostka & Spang, 2008) using rmaaddon.
rmatrain(affybatchtrain)rmatrain(affybatchtrain)
affybatchtrain |
object of class |
This function uses code from the off-CRAN package docval, version 1.0.
rmatrain returns an object of class rmatrain.
An object of class "rmatrain" is a list containing the following components:
xnorm |
matrix of RMA normalized (training) data. Observations in rows, variables in columns. |
rmadoc, sumdoc.rma, nfeature
|
information necessary for addon RMA normalization |
Roman Hornung
Kostka, D., Spang, R. (2008). Microarray based diagnosis profits from better documentation of gene expression signatures. PLoS Computational Biology 4(2):e22, <doi:10.1371/journal.pcbi.0040022>.
## Not run: # Read in example data from ArrayExpress-webpage: library("ArrayExpress") expFiles <- getAE("E-GEOD-62837", path = tempdir(), type = "raw") rawfiles <- file.path(tempdir(), expFiles$rawFiles) library("affy") # Training data: affybatchtrain <- ReadAffy(filenames=rawfiles[1:3]) try(file.remove(file.path(tempdir(), expFiles$rawFiles))) try(file.remove(file.path(tempdir(), expFiles$processedFiles))) try(file.remove(file.path(tempdir(), expFiles$sdrf))) try(file.remove(file.path(tempdir(), expFiles$idf))) try(file.remove(file.path(tempdir(), expFiles$adf))) try(file.remove(file.path(tempdir(), expFiles$rawArchive))) try(file.remove(file.path(tempdir(), expFiles$processedArchive))) # RMA normalization with documentation by value: rmaparams <- rmatrain(affybatchtrain) Xtrainnorm <- rmaparams$xnorm dim(Xtrainnorm) ## End(Not run)## Not run: # Read in example data from ArrayExpress-webpage: library("ArrayExpress") expFiles <- getAE("E-GEOD-62837", path = tempdir(), type = "raw") rawfiles <- file.path(tempdir(), expFiles$rawFiles) library("affy") # Training data: affybatchtrain <- ReadAffy(filenames=rawfiles[1:3]) try(file.remove(file.path(tempdir(), expFiles$rawFiles))) try(file.remove(file.path(tempdir(), expFiles$processedFiles))) try(file.remove(file.path(tempdir(), expFiles$sdrf))) try(file.remove(file.path(tempdir(), expFiles$idf))) try(file.remove(file.path(tempdir(), expFiles$adf))) try(file.remove(file.path(tempdir(), expFiles$rawArchive))) try(file.remove(file.path(tempdir(), expFiles$processedArchive))) # RMA normalization with documentation by value: rmaparams <- rmatrain(affybatchtrain) Xtrainnorm <- rmaparams$xnorm dim(Xtrainnorm) ## End(Not run)
This metric described in Hornung et al. (2016) was derived from the mixture score presented in Lazar et al. (2012). In contrast to the mixture score the separation score does not measure the degree of mixing but the degree of separation between the batches. Moreover it is less dependent on the relative sizes of the involved batches.
sepscore(xba, batch, k = 10)sepscore(xba, batch, k = 10)
xba |
matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
k |
integer. Number of nearest neighbors. |
For two batches j and j* (see next paragraph for the case with more batches): 1) for each observation in batch j its k nearest neighbours in both batches j and j*
simultaneously with respect to the euclidean distance are determined. Here, the proportion
of those of these nearest neighbours, which belong to batch j* is calculated; 2) the
average - denoted as MS_j - is taken over the thus obtained n_j proportions. This value
is the mixture score as in Lazar et al. (2012); 3) to obtain a measure for the separation
of the two batches the absolute difference between MS_j and its value expected in the
absence of batch effects is taken: |MS_j - n_j* /(n_j + n_j* - 1)|; 4) the separation score
is defined as the simple average of the latter quantity and the corresponding quantity when
the roles of j and j* are switched. If the supplied number k of nearest neighbours
is larger than n_j + n_j*, k is set to n_j + n_j* - 1 internally.
For more than two batches: 1) for all possible pairs of batches: calculate the metric as described above; 2) calculate the weighted average of the values in 1) with weights proportional to the sum of the sample sizes in the two respective batches.
Value of the metric
The smaller the values of this metric, the better.
Roman Hornung
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter,C., Weiss-Solís, D. Y., Duque, R., Bersini, H., Nowé, A. (2012). Batch effect removal methods for microarray gene expression data integration: a survey. Briefings in Bioinformatics 14(4):469-490, <doi:10.1093/bib/bbs037>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] sepscore(xba=Xsub, batch=batchsub, k=5) params <- ba(x=Xsub, y=ysub, batch=batchsub, method = "ratiog") Xsubadj <- params$xadj sepscore(xba=Xsubadj, batch=batchsub, k=5) params <- ba(x=Xsub, y=ysub, batch=batchsub, method = "combat") Xsubadj <- params$xadj sepscore(xba=Xsubadj, batch=batchsub, k=5)data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] sepscore(xba=Xsub, batch=batchsub, k=5) params <- ba(x=Xsub, y=ysub, batch=batchsub, method = "ratiog") Xsubadj <- params$xadj sepscore(xba=Xsubadj, batch=batchsub, k=5) params <- ba(x=Xsub, y=ysub, batch=batchsub, method = "combat") Xsubadj <- params$xadj sepscore(xba=Xsubadj, batch=batchsub, k=5)
This metric presented in Shabalin et al. (2008) is concerned with the dissimilarity across batches of the skewnesses of the observation-wise empirical distributions of the data.
skewdiv(xba, batch)skewdiv(xba, batch)
xba |
matrix. The covariate matrix, raw or after batch effect adjustment. observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
For two batches j and j* (see next paragraph for the case with more batches): 1) for each observation calculate the difference between the mean and the median of the data as a measure for the skewness of the distribution of the variable values; 2) determine the area between the two batch-wise empirical cumulative density functions of the values out of 1). The value obtained in 2) can be regarded as a measure for the disparity of the batches with respect to the skewness of the observation-wise empirical distributions.
For more than two batches: 1) for all possible pairs of batches: calculate the metric as described above; 2) calculate the weighted average of the values in 1) with weights proportional to the sum of the sample sizes in the two respective batches.
The variables are standardized before the calculation to make the metric independent of scale.
Value of the metric
The smaller the values of this metric, the better.
Roman Hornung
Shabalin, A. A., Tjelmeland, H., Fan, C., Perou, C. M., Nobel, A. B. (2008). Merging two gene-expression studies via cross-platform normalization. Bioinformatics 24(9):1154-1160, <doi:10.1093/bioinformatics/btn083>.
data(autism) skewdiv(xba=X, batch=batch) params <- ba(x=X, y=y, batch=batch, method = "ratiog") Xadj <- params$xadj skewdiv(xba=Xadj, batch=batch) params <- ba(x=X, y=y, batch=batch, method = "combat") Xadj <- params$xadj skewdiv(xba=Xadj, batch=batch)data(autism) skewdiv(xba=X, batch=batch) params <- ba(x=X, y=y, batch=batch, method = "ratiog") Xadj <- params$xadj skewdiv(xba=Xadj, batch=batch) params <- ba(x=X, y=y, batch=batch, method = "combat") Xadj <- params$xadj skewdiv(xba=Xadj, batch=batch)
Performs batch effect adjustment by standardizing the variables within batches to have zero mean and variance one.
standardize(x, batch)standardize(x, batch)
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
standardize returns an object of class standardize.
An object of class "standardize" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
nbatches |
number of batches |
batch |
batch variable |
Roman Hornung
data(autism) params <- standardize(x=X, batch=batch)data(autism) params <- standardize(x=X, batch=batch)
Performs addon batch effect adjustment for standardization:
1) takes the output of standardize applied to a training
data set together with new batch data;
2) checks whether the training data was also adjusted using standardization and whether the same number of variables is present in training and new data;
3) performs standardization on the new batch data.
standardizeaddon(params, x, batch)standardizeaddon(params, x, batch)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
batch |
factor. Batch variable of the new data. Currently has to have levels: '1', '2', '3' and so on. |
The adjusted covariate matrix of the test data.
It is not recommended to perform standardization in cross-study prediction settings, because for some classifiers the prediction performance can be strongly impaired by this method. Given a not too small test set, the following methods are recommended (Hornung et al., 2016a): combatba, meancenter, ratioa, ratiog.
Because standardization is performed "batch by batch" the "addon procedure" for standardization consists of plain standardization on the new test batches.
Roman Hornung
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
Hornung, R., Boulesteix, A.-L., Causeur, D. (2016). Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment. BMC Bioinformatics 17:27, <doi:10.1186/s12859-015-0870-z>.
data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- standardize(x=Xtrain, batch=batchtrain) Xtestaddon <- standardizeaddon(params=params, x=Xtest, batch=batchtest)data(autism) trainind <- which(batch %in% c(1,2)) Xtrain <- X[trainind,] ytrain <- y[trainind] batchtrain <- factor(as.numeric(batch[trainind]), levels=c(1,2)) testind <- which(batch %in% c(3,4)) Xtest <- X[testind,] ytest <- y[testind] batchtest <- as.numeric(batch[testind]) batchtest[batchtest==3] <- 1 batchtest[batchtest==4] <- 2 batchtest <- factor(batchtest, levels=c(1,2)) params <- standardize(x=Xtrain, batch=batchtrain) Xtestaddon <- standardizeaddon(params=params, x=Xtest, batch=batchtest)
Performs batch effect adjustment using Surrogate Variable Analysis (SVA) and additionally returns information necessary for addon batch effect adjustment with frozen SVA.
svaba(x, y, batch, nbf = NULL, algorithm = "fast")svaba(x, y, batch, nbf = NULL, algorithm = "fast")
x |
matrix. The covariate matrix. Observations in rows, variables in columns. |
y |
factor. Binary target variable. Has to have two factor levels, where each of them correponds to one of the two classes of the target variable. |
batch |
factor. Batch variable. Each factor level (or 'category') corresponds to one of the batches. For example, if there are four batches, this variable would have four factor levels and observations with the same factor level would belong to the same batch. |
nbf |
integer. Number of latent factors to estimate. |
algorithm |
character. If |
This is essentially a wrapper function of the function sva() from the Bioconductor package of the same name.
svaba returns an object of class svatrain.
An object of class "svatrain" is a list containing the following components:
xadj |
matrix of adjusted (training) data |
xtrain |
the unadjusted covariate matrix. Used in frozen SVA. |
ytrain |
binary target variable. Used in frozen SVA. |
svobj |
output of the function |
algorithm |
algorithm to use in frozen SVA |
nbatches |
number of batches |
batch |
batch variable |
Roman Hornung
Leek, J. T., Storey, J. D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics 3:1724-1735, <doi:10.1371/journal.pgen.0030161>.
Parker, H. S., Bravo, H. C., Leek, J. T. (2014). Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2:e561, <doi:10.7717/peerj.561>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] params <- svaba(x=Xsub, y=ysub, batch=batchsub)data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] params <- svaba(x=Xsub, y=ysub, batch=batchsub)
Performs addon batch effect adjustment using frozen SVA. Takes the output of performing svaba on a training data set and new batch data and correspondingly adjusts the test data to better match the adjusted training data according to the SVA model.
svabaaddon(params, x)svabaaddon(params, x)
params |
object of class |
x |
matrix. The covariate matrix of the new data. Observations in rows, variables in columns. |
The adjusted covariate matrix of the test data.
It is not recommended to perform frozen SVA in cross-study prediction settings, because it assumes similarity between training and test set and has been observed to (strongly) impair prediction performance in cases where this assumption is not given. Given a not too small test set, the following methods are recommended (Hornung et al., 2016): combatba, meancenter, ratioa, ratiog.
Roman Hornung
Leek, J. T., Storey, J. D. (2007). Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genetics 3:1724-1735, <doi:10.1371/journal.pgen.0030161>.
Parker, H. S., Bravo, H. C., Leek, J. T. (2014). Removing batch effects for prediction problems with frozen surrogate variable analysis. PeerJ 2:e561, <doi:10.7717/peerj.561>.
Hornung, R., Causeur, D., Bernau, C., Boulesteix, A.-L. (2017). Improving cross-study prediction through addon batch effect adjustment and addon normalization. Bioinformatics 33(3):397–404, <doi:10.1093/bioinformatics/btw650>.
data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) params <- svaba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain) Xsubtestaddon <- svabaaddon(params, x=Xsubtest)data(autism) # Random subset of 150 variables: set.seed(1234) Xsub <- X[,sample(1:ncol(X), size=150)] # In cases of batches with more than 20 observations # select 20 observations at random: subinds <- unlist(sapply(1:length(levels(batch)), function(x) { indbatch <- which(batch==x) if(length(indbatch) > 20) indbatch <- sort(sample(indbatch, size=20)) indbatch })) Xsub <- Xsub[subinds,] batchsub <- batch[subinds] ysub <- y[subinds] trainind <- which(batchsub %in% c(1,2)) Xsubtrain <- Xsub[trainind,] ysubtrain <- ysub[trainind] batchsubtrain <- factor(as.numeric(batchsub[trainind]), levels=c(1,2)) testind <- which(batchsub %in% c(3,4)) Xsubtest <- Xsub[testind,] ysubtest <- ysub[testind] batchsubtest <- as.numeric(batchsub[testind]) batchsubtest[batchsubtest==3] <- 1 batchsubtest[batchsubtest==4] <- 2 batchsubtest <- factor(batchsubtest, levels=c(1,2)) params <- svaba(x=Xsubtrain, y=ysubtrain, batch=batchsubtrain) Xsubtestaddon <- svabaaddon(params, x=Xsubtest)