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Machado, Victor Gordeuk, Ankit A. Summary: Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches. There are often systematic but unpredictable biases from batch-to-batch, so proper randomization of biologically relevant traits across batches is crucial for distinguishing true biological differences from experimental artifacts.

When a large of traits are biologically relevant, as is common for clinical studies of patients with varying sex, age, genotype and medical background, proper randomization can be extremely difficult to prepare by hand, especially because traits may affect biological inferences, such as differential expression, in a combinatorial manner.

Here we present ARTS automated randomization of multiple traits for study de , which aids researchers in study de by automatically optimizing batch asment for any of samples, any of traits and any batch size. Contact: mmaiensc uic. Supplementary information: Supplementary data are available at Bioinformatics online.

Data collected on high-throughput biological platforms, such as microarray and next-generation sequencing NGS , can often be processed in parallel in batches, greatly lowering the cost and time for collection. When large studies with hundreds or thousands of samples are conducted, these variations may result in statistically ificant, but biologically irrelevant, anomalies between batches Leek et al. Such batch effects can be mitigated by proper randomization of samples across batches Hu et al. A of methods exist that attempt to remove batch effects after data are already collected Johnson et al.

However, these approaches should be considered a last resort to salvage data collected after poorly randomized studies, as they must make assertions about the type of bias introduced by batches, for example using linear models to quantify distortions Leek et al. Also, these methods cannot correct for batch effect in completely unrandomized studies, for instance if all diseased samples are put into the same batch. When one or two traits are pertinent for a large study, randomization can be done manually with moderate effort.

However, patients in large clinical studies often have many relevant traits, such as sex, age, genotype, medical background and multiple measures of disease state. In such cases, proper randomization cannot reasonably be prepared by hand, and will be confounded by the likely combinatorial interaction between traits e. Here, we present the ARTS automated randomization of multiple traits for study de tool for automated study randomization.

ARTS uses a genetic algorithm to optimize an objective function based on a rigorous statistic from information theory, the mutual information. We validate ARTS using several objective functions to illustrate the versatility of the one chosen, and by showing that the genetic algorithm we use for optimization obtains a good balance between computational speed and optimization quality. We start with a motivation of our objective function. In a properly randomized study, the distribution of traits in each batch will equal the distribution of traits across all samples.

As we show in Supplementary Section S1 , this definition directly motivates the use of mutual information MI between sample traits and batch. The MI quantifies the extent to which the batch asment can predict the traits of a sample. If the MI is large, then the distribution of the traits depends strongly on the batch, and the study is not randomized; the MI of an ideally randomized study is 0.

As we discuss in more detail in Supplementary Section S2 , we should quantify the MI between both combinations of traits and the batch asment, and individual traits and the batch asment. Combinations are important when the affect of traits on biological outcomes cannot be considered independent; this is usually the case. However, in studies with a large of traits and a smaller of samples, particular combinations of traits may occur only a few times in the sample set; randomization of the combined traits becomes a trivial but useless exercise, but individual traits should still be randomized.

The MMI allows greater flexibility to accommodate studies of any size and with any of traits, as it appropriately distributes both individual traits e. We optimized randomizations for each objective function on a simulated set of samples with six binary traits and batches of size We then re-scored each randomization using each of the three objective functions.

The are shown in Figure 1 A: each panel gives the score for all three randomizations under a particular objective function. Comparing objective functions and optimization algorithms. We then re-scored each randomization using each objective function; the top panel was scored using the MMI, the middle panel using the CMI and the bottom panel using the IMI.

Note that y -axis ranges are different for each panel. B Comparison of optimized randomization score for the MMI and computing time using: i the genetic algorithm, ii a simple Monte Carlo procedure, iii random asment and iv a brute force enumeration approach, using two evenly sized batches for each sample size. Times are essentially zero for the random asment, and so are not given. In the top panel, asterisk symbols represent sample sets where the MC algorithm did not achieve the same optimized score as brute force. For A and B , error bars are standard deviations over repeated randomizations.

Thus, MMI optimization appropriately randomizes both individual and combined traits, making it appropriate for any situation regardless of the of samples and traits. In particular, Supplementary Figure S1 extends the in Figure 1 A over a range of batch sizes and different s of traits.

ARTS optimizes the MMI using a genetic algorithm GA , which iteratively refines a population of candidate batch asments through immigration, mutation and crossover, and selects the most optimal lowest MMI batch asments for subsequent generations; it is described in more detail in Supplementary Section S4.

We compare the GA to three other optimization methods, briefly described below. First, a simple Monte Carlo MC procedure generates batch asments randomly and independently, testing each and saving the best; it continues until asments have been tested without an improvement. Second, a random asment procedure simply generates a single random batching.

Third, a brute force procedure exhaustively enumerates all possible batch asments and chooses the global minimum. We compare each method in Figure 1 B, giving MMI scores for randomizing a varying of samples into two equal-sized batches in the top panel, and the computational time on a 2. For small sample sizes the MC and GA algorithms obtain equally optimal to brute force, except for two sample sizes 24 and 30 samples, indicated by asterisk in Fig. However, as sample size increases the score from MC is consistently worse and more variable than the GA, highlighted in the inset plot.

The small increase in compute time, still less than a minute for the GA with the largest sample set, is a minor trade-off for consistently better, highly reproducible optimizations. Users have several options for downloading and using ARTS, including command-line and graphical user interfaces. More details are given in Supplementary Section S5. Google Scholar. Google Preview. Oxford University Press is a department of the University of Oxford.

It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide. In. Advanced Search. Search Menu. Skip Nav Destination Article . Close mobile search Article . Volume Article Contents Abstract.

Article . Oxford Academic. Zhengdeng Lei. Vincent Gardeux. Taimur Abbasi. Roberto F. Victor Gordeuk. Ankit A. Santosh Saraf. Neil Bahroos. Yves Lussier. Associate Editor: Dr John Hancock. Select Format Select format. Permissions Icon Permissions. Abstract Summary: Collecting data from large studies on high-throughput platforms, such as microarray or next-generation sequencing, typically requires processing samples in batches.

Open in new tab Download slide. The importance of experimental de in proteomic mass spectrometry experiments: some cautionary tales. Google Scholar Crossref. Search . Adjusting batch effects in microarray expression data using empirical Bayes methods. Tackling the widespread and critical impact of batch effects in high-throughput data.

Capturing heterogeneity in gene expression studies by surrogate variable analysis. Published by Oxford University Press. All rights reserved. For Permissions, please : journals. Issue Section:. Download all slides. Supplementary data. Supplementary Data - zip file. View Metrics. alerts Article activity alert. Advance article alerts. New issue alert. Receive exclusive offers and updates from Oxford Academic. Related articles in Web of Science Google Scholar. Citing articles via Web of Science 2. Predicting correlated outcomes from molecular data. Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli.

Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR. Sparse least trimmed squares regression with compositional covariates for high dimensional data. Looking for your next opportunity?

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