You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review of single-source unsupervised domain adaptation 
Chapter Title:  Multi-site studies 
1. Abstract of Cited Paper (BIB001):  SUMMARY Non-biological experimental variation or “batch effects” are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes (>25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/. 
2. Abstract of Cited Paper (BIB002):  High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so.

Literature Review:
It is notoriously difficult to integrate data from different research groups working in the same field. The argument is that the other group uses different experimental protocols or different measuring devices, or located in a different environment, and that their data is therefore not compatible BIB003 . For example, in biostatistics, gene expression microarray data can exhibit batch effects BIB002 . These can be caused by the amplification reagent, time of day, or even atmospheric ozone level BIB001 . In some data sets, batch effects are the most dominant source of variation and are easily identified by clustering algorithms BIB002 . Domain adaptation methods are useful tools for this type of problem. Additional information such as local weather, laboratory conditions, or experimental protocol, can be exploited to correct for the batch effect. The more knowledge we have of possible confounding variables, the better the transfer from one batch to the other can be modeled. Considering the financial costs of some types of experiments, the ability to remove batch effects and integrate data sets from multiple research centers is very valuable. In general, being able to classify a target data set by training on an available source domain data set, can save a tremendous amount of time, money and effort. It would otherwise require expert annotation. But not only do adaptive classifiers save annotation costs, they can also increase statistical power. By extension, this increases the value of existing data sets. Proper cross-domain generalization techniques indirectly create a larger incentive for researchers to make their data publicly available.