Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of "sampling fraction" and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement.

Increasingly, researchers are discovering associations between microbiome and a wide range of human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. The first step towards microbiome wide association studies is the characterization of the composition of human microbiome under different conditions. Determination of differentially abundant microbes between two or more environments, known as differential abundance (DA) analysis, is a challenging and an important problem that has received considerable interest during the past decade. It is well documented in the literature that the observed microbiome data (OTU/SV table) are relative abundances with an excess of zeros. Since relative abundances sum to a constant, these data are necessarily compositional. In this article we review some recent methods for DA analysis and describe their strengths and weaknesses.

In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities are held accountable for unfair treatments arising from irresponsible data usage. Unfortunately, a lack of appropriate methodologies and tools means that even identifying unfair or discriminatory effects can be a challenge in practice.We introduce the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities.We instantiate the UA framework in FairTest, the first comprehensive tool that helps developers check data-driven applications for unfair user treatment. It enables scalable and statistically rigorous investigation of associations between application outcomes (such as prices or premiums) and sensitive user attributes (such as race or gender). Furthermore, FairTest provides debugging capabilities that let programmers rule out potential confounders for observed unfair effects.We report on use of FairTest to investigate and in some cases address disparate impact, offensive labeling, and uneven rates of algorithmic error in four data-driven applications. As examples, our results reveal subtle biases against older populations in the distribution of error in a predictive health application and offensive racial labeling in an image tagger.

The stability of proteins is reduced by urea, which is methylamine and nonprotecting osmolyte; eventually urea destabilizes the activity and function and alters the structure of proteins, whereas the stability of proteins is raised by the osmolytes, which are not interfering with the functional activity of proteins. The deleterious effect of urea on proteins has been counteracted by methylamines (osmolytes), such as trimethylamine N-oxide (TMAO), betaine, and sarcosine. To distinctly enunciate the comparison of the counteracting effects between these methylamines on urea-induced denaturation of alpha-chymotrypsin (CT), we measured the hydrodynamic diameter (d(H)) and the thermodynamic properties (T(m), DeltaH, DeltaG(U), and DeltaC(p)) with dynamic light scattering (DLS) and differential scanning calorimeter (DSC), respectively. The present investigation compares the compatibility and counteracting hypothesis by determining the effects of methylamines and urea, as individual components and in combination at a concentration ratio of 1:2 (methylamine:urea) as well as various urea concentrations (0.5-5 M) in the presence of 1 M methylamine. The experimental results revealed that the naturally occurring osmolytes TMAO, betaine, and sarcosine strongly counteracted the urea actions on alpha-chymotrypsin. The results also indicated that TMAO counteracting the urea effects on CT was much stronger than betaine or sarcosine.

To understand the biomolecular interactions of osmolytes or guanidine hydrochloride (GdnHCl) with protein functional groups, we have determined the apparent transfer free energies (Delta'(tr)) of a homologous series of cyclic dipeptides (CDs) from water to aqueous solutions of osmolytes or GdnHCl through solubility measurements, as a function of osmolyte or GdnHCl concentration at 25 degrees C under atmospheric pressure. The materials investigated in the present study included the CDs of cyclo(Gly-Gly), cyclo(Ala-Gly), cyclo(Ala-Ala), cyclo(Leu-Ala), and cyclo(Val-Val), the osmolytes of trimethylamine N-oxide (TMAO), sarcosine, betaine, proline, and sucrose, and the denaturant of GdnHCl. We observed positive values of (Delta'(tr)) for CDs from water to osmolyte, indicating that interactions between osmolytes and CDs are unfavorable. In contrast, negative (Delta'(tr)) contributions were observed for CDs from water to GdnHCl, revealing that favorable interactions are predominant. The experimental results were further used to estimate the transfer free energies (Delta'(tr)) of the peptide bond (-CONH-), the peptide backbone unit (-CH2C=ONH-), and various functional groups from water to aqueous solutions of osmolyte or GdnHCl.