A pseudo-value regression approach for differential network analysis of co-expression data

BACKGROUND

The differential network (DN) analysis identifies changes in measures of association among genes under two or more experimental conditions. In this article, we introduce a pseudo-value regression approach for network analysis (PRANA). This is a novel method of differential network analysis that also adjusts for additional clinical covariates. We start from mutual information criteria, followed by pseudo-value calculations, which are then entered into a robust regression model.

RESULTS

This article assesses the model performances of PRANA in a multivariable setting, followed by a comparison to dnapath and DINGO in both univariable and multivariable settings through variety of simulations. Performance in terms of precision, recall, and F1 score of differentially connected (DC) genes is assessed. By and large, PRANA outperformed dnapath and DINGO, neither of which is equipped to adjust for available covariates such as patient-age. Lastly, we employ PRANA in a real data application from the Gene Expression Omnibus database to identify DC genes that are associated with chronic obstructive pulmonary disease to demonstrate its utility.

CONCLUSION

To the best of our knowledge, this is the first attempt of utilizing a regression modeling for DN analysis by collective gene expression levels between two or more groups with the inclusion of additional clinical covariates. By and large, adjusting for available covariates improves accuracy of a DN analysis.

Evaluation of energy values of high-fiber dietary ingredients with different solubility fed to growing pigs using the difference and regression methods

The objective of this study was to compare the energy values of high-fiber dietary ingredients with different solubility (sugar beet pulp [SBP] and defatted rice bran [DFRB]) in growing pigs using the difference and the regression methods. A total of 21 barrows (initial BW, 40.5 ± 1.2 kg) were assigned to 3 blocks with BW as a blocking factor, and each block was assigned to a 7 × 2 incomplete Latin square design with 7 diets and two 13-d experimental periods. The 7 experimental diets consisted of a corn-soybean meal basal diet and 6 additional diets containing 10%, 20%, or 30% SBP or DFRB in the basal diet, respectively. Each of the experimental periods lasted 12 d, with a 7 d dietary adaptation period followed by 5-d total fecal and urine collection. Results showed that the digestible energy (DE) and metabolizable energy (ME) of the SBP determined by the difference method with different inclusion levels (10%, 20%, or 30%) were 2,712 and 2,628 kcal/kg, 2,683 and 2,580 kcal/kg, and 2,643 and 2,554 kcal/kg DM basis, respectively. The DE and ME in the DFRB evaluated by the difference method with 3 different inclusion levels were 2,407 and 2,243 kcal/kg, 2,687 and 2,598 kcal/kg, and 2,630 and 2,544 kcal/kg DM basis, respectively. Different inclusion levels had no effects on the energy values of each test ingredient estimated by the difference method. The DE and ME of the SBP and the DFRB estimated by the regression method were 2,562 and 2,472 kcal/kg and 2,685 and 2,606 kcal/kg DM basis, respectively. The energy values of each ingredient determined by the regression method were similar to the values estimated by the difference method with the 20% or 30% inclusion level. However, the energy values of the SBP and DFRB estimated by the difference method with the 10% inclusion level were inconsistent with the values determined by the regression method (P < 0.05). In conclusion, the regression method was a robust indirect method to evaluate the energy values for high-fiber ingredients with different solubility in growing pigs. If the number of experimental animals was limited, the difference method with a moderate inclusion level (at least 20%) of the test high-fiber ingredient in the basal diet could be applied to substitute the regression method.

Focusing on functional knee parameter determination to develop a better clinical gait analysis protocol

BACKGROUND

Attempts to improve protocol standards of marker-based clinical gait analysis (CGA) have been one of the main focuses of research to enhance robustness and reliability outcomes since the 1990s. Determining joint centres and axes constitutes an important aspect of those protocols. Although the hip joint is more prominent in such studies, knee joint center (KJC) and axis (KJA) directly affect all outcomes.

RESEARCH QUESTION

What recommendations arise from the study of the scientific literature for determining knee joint parameters (KJP) for protocols of CGA?

METHODS

A systematic, electronic search was conducted on November 2018 using three databases with the keyword combination ("functional approach" OR "functional method" OR "functional calibration") AND ("hip joint" OR "knee joint" OR "ankle joint") and analyzed by four reviewers. Given the existence of a recent review about the hip joint and the lack of material about the ankle joint, only papers about the knee joint were kept. The references cited in the selected papers were also screened in the final round of the search for these publications. The quality of the selected papers was assessed and aspects regarding accuracy, repeatability, and feasibility were thoroughly considered to allow for a comparison between studies. Technical aspects, such as marker set choice, KJP determination techniques, demographics, and functional movements, were also included.

RESULTS

Thirty-one papers were included and on average received a rating of about 75 % according to the quality scale used. The results showed that functional methods are superior or equivalent to predictive methods to estimate the KJA, while a regression method was slightly better for KJC prediction.

SIGNIFICANCE

Calibration methods should be applied to CGA whenever feasibility is reached. No study to date has focused on evaluating the in vivo RoM required to obtain reliable and repeatable results and future work should aim in this direction.

Two Powerful Tests for Parent-of-Origin Effects at Quantitative Trait Loci on the X Chromosome

Parent-of-origin effects, which describe an occurrence where the expression of a gene depends on its parental origin, are an important phenomenon in epigenetics. Statistical methods for detecting parent-of-origin effects on autosomes have been investigated for 20 years, but the development of statistical methods for detecting parent-of-origin effects on the X chromosome is relatively new. In the literature, a class of Q-XPAT-type tests are the only tests for the parent-of-origin effects for quantitative traits on the X chromosome. In this paper, we propose two simple and powerful classes of tests to detect parent-of-origin effects for quantitative trait values on the X chromosome. The proposed tests can accommodate complete and incomplete nuclear families with any number of daughters. The simulation study shows that our proposed tests produce empirical type I error rates that are close to their respective nominal levels, as well as powers that are larger than those of the Q-XPAT-type tests. The proposed tests are applied to a real data set on Turner's syndrome, and the proposed tests give a more significant finding than the Q-C-XPAT test.

Neither slim nor fat: estimating the mass of the dodo (Raphus cucullatus, Aves, Columbiformes) based on the largest sample of dodo bones to date

The dodo (Raphus cucullatus) might be the most enigmatic bird of all times. It is, therefore, highly remarkable that no consensus has yet been reached on its body mass; previous scientific estimates of its mass vary by more than 100%. Until now, the vast amount of bones stored at the Natural History Museum in Mauritius has not yet been studied morphometrically nor in relation to body mass. Here, a new estimate of the dodo’s mass is presented based on the largest sample of dodo femora ever measured (n = 174). In order to do this, we have used the regression method and chosen our variables based on biological, mathematical and physical arguments. The results indicate that the mean mass of the dodo was circa 12 kg, which is approximately five times as heavy as the largest living Columbidae (pigeons and doves), the clade to which the dodo belongs.

Estimating internal P loading in a deep water reservoir of northern China using three different methods

Much attention had been paid to reducing external loading of nutrients to improve water quality, while internal loading from sediment, which has been largely neglected, is also an important source for water eutrophication. The internal load in deep lakes or reservoirs is not easy to be detected and be quantified. In this study, three different methods (mass balance method, Fick's law, and regression equation) were combined to calculate the gross or/and net P release from sediment using limited data. Our results indicated that (1) the methods of mass balance and regression equation give similar results of sediment P release rate, with values of 0.889 and 0.902 mg m(2) d(-1), respectively, while the result of Fick's law was much lower (0.400 mg m(2) d(-1)); (2) Hot periods of sediment releasing were suggested to occur from March to April and from August to September, which correspond to periods of high risks of algae blooms. The remaining months of the year were shown as net nutrient retention; (3) for the whole region, Baihedam and Chaohekuqu were identified as zones with a higher possibility to release P from sediment. (4) P loading to the Miyun Reservoir was greater in the inflow than in the outflow, suggesting a portion of the inflow P load was retained in the water or sediment; hence, release of sediment P may continue to be a major source of phosphorus in the future.

Hamilton's rule

This paper reviews and addresses a variety of issues relating to inclusive fitness. The main question is: are there limits to the generality of inclusive fitness, and if so, what are the perimeters of the domain within which inclusive fitness works? This question is addressed using two well-known tools from evolutionary theory: the replicator dynamics, and adaptive dynamics. Both are combined with population structure. How generally Hamilton's rule applies depends on how costs and benefits are defined. We therefore consider costs and benefits following from Karlin and Matessi's (1983) "counterfactual method", and costs and benefits as defined by the "regression method" (Gardner et al., 2011). With the latter definition of costs and benefits, Hamilton's rule always indicates the direction of selection correctly, and with the former it does not. How these two definitions can meaningfully be interpreted is also discussed. We also consider cases where the qualitative claim that relatedness fosters cooperation holds, even if Hamilton's rule as a quantitative prediction does not. We furthermore find out what the relation is between Hamilton's rule and Fisher's Fundamental Theorem of Natural Selection. We also consider cancellation effects - which is the most important deepening of our understanding of when altruism is selected for. Finally we also explore the remarkable (im)possibilities for empirical testing with either definition of costs and benefits in Hamilton's rule.