You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Inferring Correlation Networks from Genomic Survey Data}
Article:
Abstract:
High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities- be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques( referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC( available at https://bitbucket. org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.
Introduction:
The study of natural communities using high throughput genomic surveys, such as 16S rRNA gene profiling, has become routine[1], yet the development of appropriate, well validated analysis methods is still ongoing. The first challenge is obtaining reliable and informative counts from 16S rRNA gene sequences by filtering spurious reads and grouping the remaining reads in a meaningful way[2],[3],[4]. Once such counts have been obtained, analysis techniques which are appropriate for discrete survey data need to be applied[5],[6][7]. A common goal of genomic surveys is to identify correlations between taxa within ecological communities. Correlation analysis provides a well trodden path to achieving this goal, but we show that it is not valid when applied to genomic survey data( GSD), and may produce misleading results. The challanges associated with GSD stem from the fact that they are a relative, rather than absolute, measure of abundances of community components. The counts comprising these data( e. g., 16S rRNA gene reads) are set by the amount of genetic material extracted from the community or the sequencing depth, and analysis typically begins by normalizing the observed counts by the total number of counts. The resulting fractions fall into a class of data termed closed or compositional, and poses its particular geometrical and statistical properties[8],[7]. Specifically, standard methods for computing correlations from GSD are theoretically invalid. Correlation estimates are biased by the fact that, since they must sum to 1, fractions are not independent and tend to have a negative correlation regardless of the true correlation between the underlying absolute abundances( termed the basis abundances)[9]. Thus, correlations estimates often reflect the compositional nature of the data, and are not indicative of the underlying biological processes[10]. In fact, in 1897 Karl Pearson warned against “attempts to interpret correlations between ratios whose numerators and denominators contain common parts”[11], and since that time it has been shown that many other standard analysis techniques are invalid when applied to such compositional data, and that their interpretation is unreliable and often misleading[10],[12],[13]. Nonetheless, these methods remain the primary tools used in studies of microbial ecology. Although approaches to compositional data analysis have been developed( e. g.[13],[14]), the basic task of inferring dependencies between components remains an outstanding challenge. A widely used method is Aitchison's test for complete subcompositional independence[15], which tests whether any dependencies are present, but does not indicate which components are correlated, nor the magnitude of the correlation. Filzmoser and Hron[16] recently developed a method for inferring correlations in compositional data after an appropriate mathematical transformation, but their method does not provide a mapping relating the correlations of the transformed variables to those of the underlying genes or species. In this paper, we first use simulations and real-world data from the Human Microbiome Project( HMP) to demonstrate that GSD can be severely biased by “compositional” effects, and then identify the factors the modulate their severity. Finally, we present a novel method, called SparCC, and show that it can infer correlations with high accuracy even in the most challenging data sets.
Discussion:
In this study we have focused on an outstanding challenge of compositional data analysis – inference of correlations. We have demonstrated that compositional effects are pronounced in 16S rRNA gene surveys of the human microbiome, and, motivated by the properties of this data, have developed a novel procedure for estimating correlations. We found that diversity of species and density of interactions are the two key factors that influence the severity of compositional effects on correlation estimates, with low diversity, high density data being the most challenging to infer correlation from using standard methods. SparCC does not rely on high diversity, rather it only requires sparsity of correlations, but in practice is robust even when the sparsity assumption is strongly violated( %30 of all component pairs are strongly correlated). Therefore, we recommend that SparCC be used on any GSD that has low diversity: as a rule of thumb we recommend an effective number of components of at least 50 for standard techniques( with the potential caveat that if strong positive correlations are present among many OTUs, the effective diversity may be much lower than estimated). We emphasize that simply having many components is not sufficient to avoid compositional effects. For example, 16S rRNA gene surveys from the HMP include hundreds to thousands of distinct OTUs, yet have have a relatively low effective number of species, with a small number of species dominating most samples. An important subclass of GSD are genome-wide surveys conducted using techniques such as DNA microarrays, RNA-seq and ChIP-seq. These genome-wide data are also subject to compositional effects, however, as these data tend to have high diversity, they are likely to be much less severe or negligible. For example, the average effective number of genes in microarray experiments available through the database[20] was for S. cerevisiae and for E. coli. This may explain why to date comparatively less attention has been paid to compositional effects in the biological sciences than in other disciplines. The preponderance of zero values are another area of concern with GSD. These zeros can represent either components that are truly absent from the community, or rare components that, by chance, were not present in the sample drawn from the community. Without additional knowledge, these options are indistinguishable, and, depending on goal of the analysis, the researcher must decide how to interpret them, and choose analysis methods accordingly. We emphasize that the treatment of zero values is a challenge that is in no way unique to compositional data, but is merely highlighted by the log-ratio transformations employed to analyze these data[21]. In this study, we eliminate zero fractions by adding small pseudocounts, as detailed in the Materials and Methods. Complementary approaches, where zeros are treated differently than non-zero values, are substantially more challenging, and are the subject of ongoing research[22]. Though the method presented in this paper allows detection of correlation within communities, many challenges still remain. First, SparCC relies on having reliable component counts, which as noted in the introduction, is not trivial. Second, the correlations estimated by SparCC measure the linear relationship between log transformed abundances. Compositional methods for inferring more general dependencies between components, equivalent to rank correlations and mutual information for non-compositional data, have not yet been developed. Third, relating the patterns detected within a community to external factors( e. g. relating the composition of a human gut microbial community to human health status), and detecting temporal patterns within and between communities requires non-standard, compositional approaches. While some such methods exist[13],[12],[23] they are rarely employed in the context of GSD, and are not tailored for its particular properties. Finally, GSD is often associated with phylogenetic information( relatedness of species or genes), which ideally would be included in the analysis( e. g. the weighted UniFrac distance, which attempts to capture differences in both abundance and phylogenetic composition of communities.). We believe that developing systematic, statistically-sound methods for such analyses of compositional GSD is a necessary step on the road to understanding the structure of biological communities, the processes by which they evolve, and the forces that shape them, and thus represents an important direction for future research.
Genomic survey data , such as those obtained from 16S rRNA gene sequencing , are subject to underappreciated mathematical difficulties that can undermine standard data analysis techniques . We show that these effects can lead to erroneous correlations among taxa within the human microbiome despite the statistical significance of the associations . To overcome these difficulties , we developed SparCC; a novel procedure , tailored to the properties of genomic survey data , that allow inference of correlations between genes or species . We use SparCC to elucidate networks of interaction among microbial species living in or on the human body .