Dataset Information

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

ABSTRACT:

Motivation

Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove batch effects for class comparison analyses. Nor can batch effects be corrected reliably in future applications of genomics-based clinical tests, in which the biological groups are by definition unknown a priori.

Results

Therefore, we assess the extent to which various batch correction algorithms remove true biological heterogeneity. We also introduce an algorithm, permuted-SVA (pSVA), using a new statistical model that is blind to biological covariates to correct for technical artifacts while retaining biological heterogeneity in genomic data. This algorithm facilitated accurate subtype identification in head and neck cancer from gene expression data in both formalin-fixed and frozen samples. When applied to predict Human Papillomavirus (HPV) status, pSVA improved cross-study validation even if the sample batches were highly confounded with HPV status in the training set.

Availability and implementation

All analyses were performed using R version 2.15.0. The code and data used to generate the results of this manuscript is available from https://sourceforge.net/projects/psva.

SUBMITTER: Parker HS

PROVIDER: S-EPMC4173013 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Publications

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Parker Hilary S HS Leek Jeffrey T JT Favorov Alexander V AV Considine Michael M Xia Xiaoxin X Chavan Sameer S Chung Christine H CH Fertig Elana J EJ

Bioinformatics (Oxford, England) 20140606 19

<h4>Motivation</h4>Sample source, procurement process and other technical variations introduce batch effects into genomics data. Algorithms to remove these artifacts enhance differences between known biological covariates, but also carry potential concern of removing intragroup biological heterogeneity and thus any personalized genomic signatures. As a result, accurate identification of novel subtypes from batch-corrected genomics data is challenging using standard algorithms designed to remove ...[more]

PMID: 24907368

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Project description:Genomic data production is at its highest level and continues to increase, making available novel primary data and existing public data to researchers for exploration. Here we explore the consequences of "batch" correction for biological discovery in two publicly available expression datasets. We consider this to include the estimation of and adjustment for wide-spread systematic heterogeneity in genomic measurements that is unrelated to the effects under study, whether it be technical or biological in nature.We present three illustrative data analyses using surrogate variable analysis (SVA) and describe how to perform artifact discovery in light of natural heterogeneity within biological groups, secondary biological questions of interest, and non-linear treatment effects in a dataset profiling differentiating pluripotent cells (GSE32923) and another from human brain tissue (GSE30272).Careful specification of biological effects of interest is very important to factor-based approaches like SVA. We demonstrate greatly sharpened global and gene-specific differential expression across treatment groups in stem cell systems. Similarly, we demonstrate how to preserve major non-linear effects of age across the lifespan in the brain dataset. However, the gains in precisely defining known effects of interest come at the cost of much other information in the "cleaned" data, including sex, common copy number effects and sample or cell line-specific molecular behavior.Our analyses indicate that data "cleaning" can be an important component of high-throughput genomic data analysis when interrogating explicitly defined effects in the context of data affected by robust technical artifacts. However, caution should be exercised to avoid removing biological signal of interest. It is also important to note that open data exploration is not possible after such supervised "cleaning", because effects beyond those stipulated by the researcher may have been removed. With the goal of making these statistical algorithms more powerful and transparent to researchers in the biological sciences, we provide exploratory plots and accompanying R code for identifying and guiding "cleaning" process (https://github.com/andrewejaffe/StemCellSVA). The impact of these methods is significant enough that we have made newly processed data available for the brain data set at http://braincloud.jhmi.edu/plots/ and GSE30272.

Project description:Microarray batch effect (BE) has been the primary bottleneck for large-scale integration of data from multiple experiments. Current BE correction methods either need known batch identities (ComBat) or have the potential to overcorrect, by removing true but unknown biological differences (Surrogate Variable Analysis SVA). It is well known that experimental conditions such as array or reagent batches, PCR amplification or ozone levels can affect the measured expression levels; often the direction of perturbation of the measured expression is the same in different datasets. However, there are no BE correction algorithms that attempt to estimate the individual effects of technical differences and use them to correct expression data. In this manuscript, we show that a set of signatures, each of which is a vector the length of the number of probes, calculated on a reference set of microarray samples can predict much of the batch effect in other validation sets. We present a rationale of selecting a reference set of samples designed to estimate technical differences without removing biological differences. Putting both together, we introduce the Batch Effect Signature Correction (BESC) algorithm that uses the BES calculated on the reference set to efficiently predict and remove BE. Using two independent validation sets, we show that BESC is capable of removing batch effect without removing unknown but true biological differences. Much of the variations due to batch effect is shared between different microarray datasets. That shared information can be used to predict signatures (i.e. directions of perturbation) due to batch effect in new datasets. The correction can be precomputed without using the samples to be corrected (blind), done on each sample individually (single sample) and corrects only known technical effects without removing known or unknown biological differences (conservative). Those three characteristics make it ideal for high-throughput correction of samples for a microarray data repository. We also compare the performance of BESC to three other batch correction methods: SVA, Removing Unwanted Variation (RUV) and Hidden Covariates with Prior (HCP). An R Package besc implementing the algorithm is available from http://explainbio.com.

Dataset Information

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Motivation

Results

Availability and implementation

Publications

Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction.

Similar Datasets

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