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Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics
© The Author(s). 2017
Published: 14 March 2017
In proteomics, batch effects are technical sources of variation that confounds proper analysis, preventing effective deployment in clinical and translational research.
Using simulated and real data, we demonstrate existing batch effect-correction methods do not always eradicate all batch effects. Worse still, they may alter data integrity, and introduce false positives. Moreover, although Principal component analysis (PCA) is commonly used for detecting batch effects. The principal components (PCs) themselves may be used as differential features, from which relevant differential proteins may be effectively traced. Batch effect are removable by identifying PCs highly correlated with batch but not class effect.
However, neither PC-based nor existing batch effect-correction methods address well subtle batch effects, which are difficult to eradicate, and involve data transformation and/or projection which is error-prone. To address this, we introduce the concept of batch-effect resistant methods and demonstrate how such methods incorporating protein complexes are particularly resistant to batch effect without compromising data integrity.
Protein complex-based analyses are powerful, offering unparalleled differential protein-selection reproducibility and high prediction accuracy. We demonstrate for the first time their innate resistance against batch effects, even subtle ones. As complex-based analyses require no prior data transformation (e.g. batch-effect correction), data integrity is protected. Individual checks on top-ranked protein complexes confirm strong association with phenotype classes and not batch. Therefore, the constituent proteins of these complexes are more likely to be clinically relevant.
The emergence of high-performance protein-extraction procedures (e.g., PCT ), brute-force spectra-capture methods (e.g., SWATH ), and improved multiplexing technologies  has transformed proteomics (the high-throughput expressional study of proteins) from a relatively low-throughput technology to one with critical practical applications in biology.
The application of proteomics on clinical samples (i.e., clinical proteomics) is concerned with unraveling proteome changes associated with disease using actual clinical samples. Typically, two classes of samples---e.g., normal (D) and disease (D*)---are compared against each other. Proteins exhibiting strong inter-class differences are marked as differential and analyzed for relevant functional roles. Statistics provides powerful means for differential protein selection based on the hypothesis-testing framework. This process is commonly referred to as “feature selection” (where a feature is a protein in this instance; see Methods for details on feature selection).
Unfortunately, despite increasing ease in data generation, extracting knowledge from proteomics expression data is difficult . Proper feature selection, if done correctly, should lead directly to drug-target and biomarker identification; but in practice, this is seldom the case [5, 6].
In theory, a strongly differential feature (e.g., a protein) should exhibit strong inter-class differences across samples. However, real samples are intrinsically noisy. This intrinsic noise is random (unstructured) and obfuscates proper feature selection by masking true inter-class differences. The manner in which the samples are prepared contributes towards a second type of variation, which unlike intrinsic random noise, is non-random (structured) and not associated with class effects; i.e., they do not distinguish sample classes D and D* specifically. This second source of variation, where features are more strongly correlated with technical factors (time of experiment, technician, reagent vendor, instrument, etc.) than with sample classes (e.g., D and D*) [7–10], is referred to as batch effects.
It is not straightforward to distinguish batch and class effects: When the former is mild, it may lead to bias during feature selection; but when strong, lead to downright selection of irrelevant proteins that confound and mislead (i.e., false positives) and/or the loss of truly relevant proteins (i.e., false negatives). In other words, batch effects obfuscate analysis. Batch effects are known to be present in genomics assays [7–9]. However, they are a nuisance in proteomics assays, where multiplexing limits impose constraints on the number of samples for concurrent analysis; e.g., analyzing eight samples with the commonly used 4-plex iTRAQ labeling system requires at least two separate experiments performed at different times, or on different instruments.
Despite fairly recent work demonstrating that batch-effect correction may lead to substantial increase in feature-selection sensitivity , a systematic exploration of batch effects in proteomics data, and proposal of feasible workarounds, is missing. Reasons include underestimating heterogeneity in practical usage (assuming that class effects dominate variation), unsuitable data (data are already match-paired as ratios and thus, classes cannot be distinguished from each other), and the erroneous belief that normalization eradicates batch effects. Normalization is a data processing technique that adjusts global properties of measurements for individual samples for appropriate comparisons. Examples include z- and quantile-normalization, and mean-scaling. However, normalization cannot eradicate batch effects, as the latter does not affect all variables similarly . In cases where statistical assumptions are violated, normalization may affect data integrity instead.
Batch effects are usually detected via principal component analysis (PCA), where the first two or three principal components (PCs) are plotted for each sample colored by the batch labels, and separation of colors taken as evidence of batch effects . When batch effects are dominant, the first n PCs are expected to be dominated by batch effects, and removal of these PCs may be an alternative yet effective means of batch-effect correction. The remaining PCs---though these have lower contribution towards overall variation---may be dominated by small subsets of variables with good class-discrimination power . Thus, feature selection at the level of PCs---i.e., using PCs, as opposed to proteins, as features---may be a viable batch effect-resistant feature-selection strategy.
Protein complex-based analysis, as a new analytical paradigm, provides a powerful yet stable means of selecting features, at the level of protein complexes, from proteomics data [14–17]. Protein complexes are strongly enriched for biological coherence signal , beating any combinations of alternative measurements (expression correlation, GO-term overlaps, etc.) Using protein complex-based analysis, we have successfully recovered missing proteins  and overcome consistency issues where patient samples present widely different protein sets [19–21]. Protein complex-based analysis also exhibits unparalleled stability and reproducibility in feature selection [14, 22, 23]. We hypothesize that this superior performance may stem in part, from innate resistance to batch effects.
We address the following gaps in batch effects, and its implications for feature selection in a proteomics setting. First, we propose a simple technique for simulating batch effects in proteomics data, and recommend using it for evaluating feature-selection procedures, as well as checking whether batch effect-correction algorithms are working as intended. Second, while PCA is the de facto approach for visualizing presence of batch effects, we investigate its feasibility as a feature-selection technique where features are principal components (PCs) instead of proteins. And finally, as a potential new advantage (which is never reported before), we check whether protein complex-based feature-selection algorithms are truly resistant to batch effects; and if so, whether they may supersede the need for batch effect-correction algorithms.
Simulated data --- D2.2 and D2.2H (Simulated batch effect)
We used part of the D2.2 dataset (301 to 400) from the study of Langley and Mayr as a reference proteomics simulation dataset where differential variables are known a priori  (four samples in class D and D* respectively). Quantitation is based on spectral counts.
Class effects and batch effects are inserted randomly, with the increase made in D* samples only. Simulated data with both class and batch effects inserted is referred to as D2.2H, while the original data with only class effects is referred to as D2.2 (Additional file 1).
Real data --- Renal cancer (RC) (Real batch effect)
The renal cancer (RC) study of Guo et al.  comprises a total of 24 SWATH runs originating from six pairs of non-tumorous and tumorous clear-cell renal cell carcinoma (ccRCC) tissues, in two batches, rep1 and rep2 (Additional file 1).
Batch effect-correction methods
For batch-effect correction, we used quantile normalization and linear-scaling as generic approaches (Additional file 1). Quantile normalization and linear-scaling are not explicitly batch effect-correction methods. So, we also used COMBAT on D2.2H to remove batch effects, and evaluate performance recovery against the original D2.2 (where no batch effects are introduced but class effects are). COMBAT is a well-known batch effect-correction approach and employs empirical Bayes frameworks for adjusting data for batch effects. It is reported to be robust to outliers in small sample sizes (<25) while maintaining comparable performance to existing methods for large samples .
Statistical feature-selection methods
Four classes of feature-selection methods are tested to see whether they are robust against batch effects (i.e., they do not select features that are associated with batch). The standard Single-Protein t-test (SP)  and Hypergeometric Enrichment (HE)  test are the most commonly used comparative analysis methods. We have also included two variants of rank-based network algorithms (RBNAs)---viz. SubNETs (SNET)  and Fuzzy-SubNETs (FSNET) ---which were demonstrated to be highly stable and reliable  (Additional file 1).
On real data, HE, SNET and FSNET are tested using CORUM complexes  as their protein complex-based feature vector [16, 17, 29]. The performance of these feature-selection methods are evaluated on precision and recall (Additional file 1).
On simulated data (D2.2 and D2.2H, without and with simulated batch effects respectively), these same methods are evaluated based on simulated complexes (pseudo-complexes). In simulated data, the differential proteins are known a priori. We use these to create true-positive pseudo-complexes. To achieve this, a Euclidean distance is first determined for all differential protein pairs across all samples. These are then clustered via Ward’s linkage. Differential proteins are reordered such that those with similar expression pattern (across samples) are adjacent to each other. This reordered list is then split at regular intervals to generate 101 true-positive pseudo-complexes. An equal number of non-significant proteins is randomly selected, reordered based on expressional correlation, and then split to generate an equal number of true-negative pseudo-complexes.
We may alter the “purity” of the true-positive pseudo-complexes by reducing the proportion of differential proteins within them. In practice, we seldom observe all complex members being differentially expressed simultaneously (which also renders it too easy for detection). Purity, therefore, is the proportion of differential proteins within each true-positive pseudo-complex. At 100% purity, simulated complexes are comprised solely of significant proteins; at 75% purity, 25% of the constituent significant proteins are randomly replaced with non-significant ones; and so on. Reducing purity permits evaluation of the robustness and sensitivity of the complex-based analysis methods. Purity is tested at three levels: 100, 75 and 50%.
The true-positive and true-negative pseudo-complexes are combined into a single vector. Evaluation is based on the F-score.
Results and discussion
Batch effects cannot be completely eradicated via batch effect-correction algorithms
The commonly used batch effect-correction method, COMBAT , only partially recovers original test performance (without batch effects). Therefore, it does not completely eradicate heterogeneity. Additionally, while it improves overall performance, it also tends to reduce precision, incorporating false positives into the selected feature set. This is cause for concern during selection of features for experimental validation. In the non-FDR corrected scenario, COMBAT also does not perform better than conventional data normalization methods, e.g., quantile normalization and linear-scaling (Fig. 2). However, when test requirements are more stringent given the 5% FDR cutoff, then it is clear that COMBAT provides considerable advantage (over both quantile normalization and linear-scaling).
In spite of the simplicity of these simulations, it is noteworthy that batch effect-removal (and normalization) methods are not a panacea. We cannot declare COMBAT is inferior, but rather, we will never know if batch effects have been effectively removed from real data, particularly when the data happens not to fit COMBAT’s assumptions well. Thus, a naïve reliance on batch effect-correction algorithms, without conducting further downstream checks for remnant batch effects (if possible), may potentially worsen analytical outcome.
A relook at principal component analysis for detecting and removing batch effects
Principal component analysis (PCA) yields linear combinations of each variable’s contribution to variance, but evidently not all variables are equally interesting or relevant (which necessitates feature selection in the first place): We may say the features that changed the most, i.e., exhibited the most variation, are likely more impactful (contributing strongly to class or batch effects, or both). Although we may not know this first-up for every feature, we can still reduce the feature set size via variance-based pre-selection. Here, a cutoff is introduced to include only the top 20% proteins (ranked by variance) and used in PCA (Fig. 1b).
Scatterplots, being rough visual guides, do not reveal well the contributions of class and batch effects to each PC. In our opinion, paired boxplots (splitting each PC by class and batch) are more informative and, here, it is evident that PC1 and PC2 correspond to batch and class effects respectively (Fig. 3b).
Using two examples (D2.2. 301H and 302H), we show that removal of the first PC (PC1) allows samples to cluster based on classes rather than batch (Fig. 4a). A caveat is that removal of PC1 works here primarily because it is strongly correlated with batch effects; i.e., batch effects account for the majority of variance in the data. On real data, it may necessitate the removal of several other PCs that are correlated with batch effects. Moreover, if incompletely eradicated or inseparable from class effects, batch effects may resurface in subsequent PCs (after PC1) during analysis (See section “Variable-selection methods with resistance to batch/heterogeneity effects”).
Suppose that removal of the first n PCs results in good class separation in PCA, it may be possible to use the remaining PCs for feature selection and non-projection-based clustering techniques, e.g., hierarchical clustering and k-means. This may seem counter-intuitive, as during standard analysis involving PCA, it is common to keep just the top n PCs accounting for the majority of variation. But not all variation is attributable towards class effects (even if it is large). Moreover, PCs with large same-sign coefficients tend to represent non-class effect properties correlated with the variables; e.g., Tsuchiya et al. demonstrated that, for their dataset, PC1 is linearly correlated with the magnitude of average gene expression . On the other hand, subsequent PCs with lower contribution towards overall variation may be dominated by small subsets of variables with good class-discrimination power . Thus, instead of discarding the lower ranked PCs, it is more reasonable to remove the top PCs that are non-correlated with class effects. We find that subsequent PCs do correlate strongly with sample classes D and D* (Fig. 4a), and may be used as variables for clustering (Fig. 4b).
Effects on precision and recall for D2.2.301H and D2.2.302H before and following removal of proteins with heavy loadings on the first principal component (PC1 and − PC1 respectively)
This procedure---viz. rank proteins by variance, perform PCA using the top 20%, discard PCs that are strongly correlated with technical variables, and perform e.g., clustering using the remaining PCs---may be used for class prediction on new batches with unknown class labels. A schematic is provided in Fig. 1c. And, to test this, two different sets of batch effects are inserted into D2.2.301, the first is (20, 50, 80, 100 and 200%), and the second is (10%, 30%, 50%, 70%, 90%) (Fig. 4c), and the data combined.
Expectedly, clustering based on all PCs show that batch effects dominate. However, removal of PC1 recovers perfect class discrimination. This suggests that even when combining several datasets with different batch effects, removal of PC1 retains class effects, and permits class prediction (Fig. 1c). Moreover, if we have multiple datasets of the same disease, properly dealing with batch effects makes it possible to pool these datasets for analysis. This is useful when larger sample sizes are needed for ad hoc analysis.
Using variance-based variable pre-selection and principal component manipulation to tackle real batch effects
To evaluate how the procedure above is applicable towards real data, we consider the renal cancer study of Guo et al., which contains two technical replicates (i.e., two batches, rep1 and rep2). This data, RC, has been carefully processed; and batch effects appear contained (Additional file 4).
Feature-selection methods with resistance to batch/heterogeneity effects
There are remnant batch effects in RC (Fig. 5d) that are difficult to eradicate, and may lead to bias during subsequent feature selection. When batch effects are strong, then removal of the first few PCs is a useful direct strategy, especially if information on batch and other potential confounding factors are not known a priori (i.e., we cannot systematically eliminate non-class relevant variation) and batch effect-correction methods cannot be effectively deployed. On the other hand, it is not always straightforward to interpret the PCs and extract the proteins relevant for class effects. PC-based removal and batch-effect correction also may not be able to remove subtle batch effects.
As an additional note of caution, batch effect-removal approaches---including the procedure described above---may at times be overkill: These corrections may unintentionally eliminate true biological heterogeneity amongst samples (i.e., disease subpopulations), which is informative (e.g., identifying personalized signatures for determining therapy) and should not be discarded from the data in the first place. Unfortunately, batch-effect and subpopulations are not easy to tell apart . And if we run the PC-removal or other batch effect-correction methods, subpopulation information is irrevocably lost. On the other hand, high heterogeneity in the form of multiple subpopulations can make analysis very challenging, particularly in cancer proteomics .
One way forward is to incorporate robust data normalization methods and biological context (e.g., networks and protein complexes) directly into feature-selection approaches . Recently, we expound on the advantages of protein complexes as suitable biological context in improving data analysis. Unlike analysis at the level of proteins as features, the use of protein complexes as features, leads to improve stability and reproducibility [14, 15, 20, 21, 32, 33].
We are curious if the high performance of protein complex-based methods belonging to the family known as Rank-Based Network Analysis (RBNAs) exhibit superior performance (high feature-selection reproducibility and cross-validation prediction power) due to innate resistance to batch effects [14, 23, 27]. There are several reasons why we think RBNAs may be robust against batch effects: Its score function uses rank-based discretization instead of exact values, which is robust against various biases, e.g., test-set bias . Use of biological context (e.g., networks and complexes) increases biological signal over signal from other spurious correlations (e.g., batch) as only signal from same-complex members are summated. We already know that use of protein complexes increases power, and we believe the signal amplification is phenotypically relevant . Previous tests have already demonstrated that complex-based features are specifically predictive for phenotype classes and that false-positive rates are low. However, a specific investigation into batch-effect resistance has not been done. Hence, we test two members: SubNets (SNET)  and Fuzzy-SNET (FSNET) .
To test whether RBNAs are effective in overcoming batch effects, as opposed to simply eliminating them, we performed two sets of tests; the first on simulated data (based on D2.2 and D2.2H) as a proof-of-concept and the second on real data (using RC). Besides SNET  and FSNET  (two representative RBNA methods), we also include the standard single-protein t-test (SP) [26, 36] and the hypergeometric enrichment test (HE) . SP is a control based on the standard univariate t-test at the level of individual proteins. HE is an over-representation-based technique meant to determine if the differential proteins are significantly enriched in some protein complex based on the hypergeometric test; i.e., it uses the same protein complexes, but not the same statistical test as the RBNAs.
Our findings are a positive indication that the RBNAs are highly robust against weaker differential signal and batch effects. Additionally, given HE’s poor performance, we assert that use of complexes alone is insufficient; the statistical test setup is also critical. This result is important as it is the first, to the best of our knowledge, to demonstrate that complex-based feature-selection approaches are resilient against batch effects.
However, there are caveats: Firstly, these batch effects are simulated, and unreflective of true batch effects. Secondly, these pseudo-complexes may not be good approximations of true protein complexes. Although we cannot test real data directly (the real differential proteins are not known a priori), we may still evaluate these methods (SP, HE, SNET and FSNET) on real data.
This finding is critical: Since RBNAs are robust against batch effects, this obviates the need for performing data transformations (e.g., PCA or batch-effect correction). This also means that if subpopulations do exist in the data, this information is retained. It should be noted that dealing with subpopulations is difficult and outside the scope of this work, although we may also use complexes for detailed in-depth study of subpopulations .
While RBNAs are evidently powerful against batch effects, especially against subtle ones that cannot be easily removed via removal of the first n PCs or via batch effect-correction algorithms, they are not perfect solutions. E.g. methods such as FSNET weigh each protein in a protein complex by the fraction of subjects (in the relevant class) where the protein is highly ranked. This fraction may be unstable from subsample to subsample, particularly in the presence of hidden subpopulations. This may reduce class-specific signals, making it difficult to identify good-quality and relevant features.
Downstream considerations post-analysis
Discarding batch effect-laden PCs is a transformation that provides a transformed dataset with much reduced batch effects. It helps identify strong class discriminatory features. Yet, at the same time, new batches are not directly comparable to this transformed dataset. So it is difficult to extract directly clinical guideline/thresholds for future diagnostic use. For example, as a simple diagnostic tool, one wants a protein X such that: If X’s abundance is above a threshold y, then the patient is sick. But, in the presence of batch effects, different thresholds are needed for different batches. On the other hand, once the good features are found, one may apply more reliable technologies---i.e., ones that are less susceptible to batch effects---to measure only those features specifically.
The impact of batch effects in proteomics cannot be understated, and has key implications in clinical and translational research. We have shown that batch effect-correction algorithms are not a panacea, and that the corrected data may be erroneous. Moreover, with the development of any novel feature-selection approach, it is worthwhile to test their robustness against simulated batch effects.
We have illustrated that side-by-side barplots are better for visually detecting batch effects than the standard PCA scatterplot-based representation format. Moreover, the PCs themselves may be used as features, which may also be effectively traced back to relevant differential proteins. This is also a viable strategy complementary to batch effect-correction methods.
Unfortunately, subtle batch effects cannot be easily removed or detected, and can lead to bias in the analysis of real data. Moreover, data transformation may lead to the loss of valuable subpopulation information. We confirm that one of the reasons complex-based algorithms like the RBNAs are successful is because they have innate resistance against batch effects. This resistance stems from amplification of phenotypic-relevant signal from same-complex members and rank-based discretization of expression values (increasing the signal from high confidence proteins while removing noise from low confidence proteins). As RBNAs require no prior data transformations, the integrity of the data is preserved (including subpopulation information). Finally individual check on the top features selected by RBNAs confirms that they are strongly associated with class and not batch effects. This means that features selected in this manner are more likely to be clinically useful.
LW gratefully acknowledges support by a Kwan-Im-Thong-Hood-Cho-Temple chair professorship.
Publication charges for this article have been funded by an education grant (290-0819000002) to WWBG from the School of Pharmaceutical Science and Technology, Tianjin University, China.
Availability of data and materials
All data generated or analysed during this study are included in this published article under the Additional files section.
WWBG and LW designed the bioinformatics methods and pipelines, performed implementation and wrote the manuscript. Both authors read and approved the final manuscript.
The authors decalare that they have no competing interests.
Consent for publication
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About this supplement
This article has been published as part of BMC Genomics Volume 18 Supplement 2, 2017. Selected articles from the 15th Asia Pacific Bioinformatics Conference (APBC 2017): genomics. The full contents of the supplement are available online http://bmcgenomics.biomedcentral.com/articles/supplements/volume-18-supplement-2.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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