CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12 000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpson's paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

Between-Level Incongruences in Human Positivity

Humans now understand the world as multilevel in nature. For example, societies emerge from individuals, and general experiences of life consist of specific aspects and momentary episodes. A critical feature of multilevel phenomena is between-level incongruences. Applied to human positivity, this means that positive higher-level units are not simply composed of positive lower-level units and that what is good for lower-level units may not be good for higher-level units (and vice versa). For example, killjoys may improve societal well-being, personal achievement may require giving up on certain goals, and a happy life may not arise from simply happy moments. In this article, I provide examples (organized by the positive outcome of well-being and performance and by the social, structural, and temporal forms of multilevel phenomena) to show that such between-level incongruences are ubiquitous. Next, I analyze a few mechanisms that may govern the diverse instantiations of between-level incongruences in positivity. Finally, I discuss implications of this perspective, such as why positivity claims should always qualify their level of analysis; how psychological science may benefit from a multilevel, dynamical, and computational perspective; and how to improve human positivity in light of between-level incongruences.

Simpson's paradox and the impact of donor-recipient race-matching on outcomes post living or deceased donor kidney transplantation in the United States

Background

Race is a prognostic indicator in kidney transplant (KT). However, the effect of donor-recipient race-matching on survival after KT remains unclear.

Methods

Using the United Network for Organ Sharing (UNOS) database, a retrospective study was conducted on 244,037 adults who received first-time, kidney-alone transplantation between 2000 and 2019. All patients were categorized into two groups according to donor-recipient race-matching, and the living and deceased donor KT (LDKT and DDKT) were analyzed in subgroups.

Results

Of the 244,037 patients, 149,600 (61%) were race-matched, including 107,351 (87%) Caucasian, 20,741 (31%) African Americans, 17,927 (47%) Hispanics, and 3,581 (25%) Asians. Compared with race-unmatching, race-matching showed a reduced risk of overall mortality and graft loss (unadjusted hazard ratio (HR) 0.86, 95% confidence interval (CI) 0.84-0.87; and unadjusted HR 0.79, 95% CI: 0.78-0.80, respectively). After propensity score-matching, donor-recipient race-matching was associated with a decreased risk of overall graft loss (P < 0.001) but not mortality. In subgroup analysis, race-matching was associated with higher crude mortality (HR 1.12, 95% CI: 1.06-1.20 in LDKT and HR 1.11, 95% CI: 1.09-1.14 in DDKT). However, race-matching was associated with a decreased risk of graft loss in DDKT (unadjusted HR 0.97, 95% CI: 0.96-0.99), but not in LDKT. After propensity score-matching, race-matching had better outcomes for LDKT (patient survival, P = 0.047; graft survival, P < 0.001; and death-censored graft survival, P < 0.001) and DDKT (death-censored graft survival, P = 0.018). Nonetheless, race-matching was associated with an increased adjusted mortality rate in the DDKT group (P < 0.001).

Conclusion

Race-matching provided modest survival advantages after KT but was not enough to influence organ offers. Cofounding factors at baseline led to a contorted crude conclusion in subgroups, which was reversed again to normal trends in the combined analysis due to Simpson's paradox caused by the LDKT/DDKT ratio.

Quasi-Simpson paradox in estimating the expected mortality rate from the SARS-CoV-2

On January 30, 2020, the World Health Organization (WHO) declared SARS-CoV-2 a global pandemic, based on a high infection rate and a high case fatality rate (CFR). The combination of these two points led WHO to forecast a high expected mortality rate of approximately 2% of the population. The phenomenon of Simpson's paradox teaches us that we should be careful when we combine two variables together. Indeed, despite the high mortality rate in several places, this forecast seems to have collapsed. We believe one of the reasons for the erroneous forecasts is that combining the above points ignored a confounding variable - many of the virus carriers are asymptomatic and therefore not diagnosed.

Severely malnourished children with a low weight-for-height have similar mortality to those with a low mid-upper-arm-circumference: II. Systematic literature review and meta-analysis

BACKGROUND

The WHO recommended criteria for diagnosis of sever acute malnutrition (SAM) are weight-for-height/length Z-score (WHZ) of <- 3Z of the WHO₂₀₀₆ standards, a mid-upper-arm circumference (MUAC) of < 115 mm, nutritional oedema or any combination of these parameters. A move to eliminate WHZ as a diagnostic criterion has been made on the assertion that children with a low WHZ are healthy, that MUAC is a "superior" prognostic indicator of mortality and that adding WHZ to the assessment does not improve the prediction of death. Our objective was to examine the literature comparing the risk of death of SAM children admitted by WHZ or MUAC criteria.

METHODS

We conducted a systematic search for reports which examined the relationship of WHZ and MUAC to mortality for children less than 60 months. The WHZ, MUAC, outcome and programmatic variables were abstracted from the reports and examined. Individual study's case fatality rates were compared by chi-squared analysis and random effects meta-analyses for combined data.

RESULTS

Twenty-one datasets were reviewed. All the patient studies had an ascertainment bias. Most were inadequate because they had insufficient deaths, used obsolete standards, combined oedematous and non-oedematous subjects, did not report the proportion of children with both deficits or the deaths occurred remotely after anthropometry. The meta-analyses showed that the mortality risks for children who have SAM by MUAC < 115 mm only and those with SAM by WHZ < -3Z only are not different.

CONCLUSIONS

As the diagnostic criteria identify different children, this analysis does not support the abandonment of WHZ as an important independent diagnostic criterion for the diagnosis of SAM. Failure to identify such children will result in their being denied treatment and unnecessary deaths from SAM.

Severely malnourished children with a low weight-for-height have a higher mortality than those with a low mid-upper-arm-circumference: I. Empirical data demonstrates Simpson's paradox

BACKGROUND

According to WHO childhood severe acute malnutrition (SAM) is diagnosed when the weight-for-height Z-score (WHZ) is <-3Z of the WHO2006 standards, the mid-upper-arm circumference (MUAC) is < 115 mm, there is nutritional oedema or any combination of these parameters. Recently there has been a move to eliminate WHZ as a diagnostic criterion on the assertion that children meeting the WHZ criterion are healthy, that MUAC is universally a superior prognostic indicator of mortality and that adding WHZ to the assessment does not improve the prediction; these assertions have lead to a controversy concerning the role of WHZ in the diagnosis of SAM.

METHODS

We examined the mortality experience of 76,887 6-60 month old severely malnourished children admitted for treatment to in-patient, out-patient or supplementary feeding facilities in 18 African countries, of whom 3588 died. They were divided into 7 different diagnostic categories for analysis of mortality rates by comparison of case fatality rates, relative risk of death and meta-analysis of the difference between children admitted using MUAC and WHZ criteria.

RESULTS

The mortality rate was higher in those children fulfilling the WHO2006 WHZ criterion than the MUAC criterion. This was the case for younger as well as older children and in all regions except for marasmic children in East Africa. Those fulfilling both criteria had a higher mortality. Nutritional oedema increased the risk of death. Having oedema and a low WHZ dramatically increased the mortality rate whereas addition of the MUAC criterion to either oedema-alone or oedema plus a low WHZ did not further increase the mortality rate. The data were subject to extreme confounding giving Simpson's paradox, which reversed the apparent mortality rates when children fulfilling both WHZ and MUAC criteria were included in the estimation of the risk of death of those fulfilling either the WHZ or MUAC criteria alone.

CONCLUSIONS

Children with a low WHZ, but a MUAC above the SAM cut-off point are at high risk of death. Simpson's paradox due to confounding from oedema and mathematical coupling may make previous statistical analyses which failed to distinguish the diagnostic groups an unreliable guide to policy. WHZ needs to be retained as an independent criterion for diagnosis of SAM and methods found to identify those children with a low WHZ, but not a low MUAC, in the community.

Transformative Opportunities for Single-Cell Proteomics

Many pressing medical challenges, such as diagnosing disease, enhancing directed stem-cell differentiation, and classifying cancers, have long been hindered by limitations in our ability to quantify proteins in single cells. Mass spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single-cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.

Obesity paradox in heart failure: statistical artifact, or impetus to rethink clinical practice?

The "obesity paradox" in heart failure (HF) is a phenomenon of more favorable prognosis, especially better survival, in obese versus normal-weight HF patients. Various explanations for the paradox have been offered; while different in their details, they typically share the premise that obesity per se is not actually the cause of reduced mortality in HF. Even so, there is a lingering question of whether clinicians should refrain from, or at least soft-pedal on, encouraging weight loss among their obese HF patients. Against the backdrop of recent epidemiological analysis by Banack and Kaufman, which speculates that collider stratification bias may generate the obesity paradox, we seek to address the aforementioned question. Following a literature review, which confirms that obese HF patients are demographically and clinically different from their normal-weight counterparts, we present four hypothetical data sets to illustrate a spectrum of possibilities regarding the obesity-mortality association. Importantly, these hypothetical data sets become indistinguishable from each other when a crucial variable is unmeasured or unreported. While thorough, the discussion of these data sets is intended to be accessible to a wide audience, especially including clinicians, without a prerequisite of familiarity with advanced epidemiology. We also furnish intuitive visual diagrams which depict a version of the obesity paradox. These illustrations, along with reflection on the distinction between weight and weight loss (and, furthermore, between voluntary and involuntary weight loss), lead to our recommendation for clinicians regarding the encouragement of weight loss. Finally, our conclusion explicitly addresses the questions posed in the title of this article.

What Constitutes Science and Scientific Evidence: Roles of Null Hypothesis Testing

We briefly discuss the philosophical basis of science, causality, and scientific evidence, by introducing the hidden but most fundamental principle of science: the similarity principle. The principle's use in scientific discovery is illustrated with Simpson's paradox and other examples. In discussing the value of null hypothesis statistical testing, the controversies in multiple regression, and multiplicity issues in statistics, we describe how these difficult issues should be handled based on our interpretation of the similarity principle.

Gene Ontology: Pitfalls, Biases, and Remedies

The Gene Ontology (GO) is a formidable resource, but there are several considerations about it that are essential to understand the data and interpret it correctly. The GO is sufficiently simple that it can be used without deep understanding of its structure or how it is developed, which is both a strength and a weakness. In this chapter, we discuss some common misinterpretations of the ontology and the annotations. A better understanding of the pitfalls and the biases in the GO should help users make the most of this very rich resource. We also review some of the misconceptions and misleading assumptions commonly made about GO, including the effect of data incompleteness, the importance of annotation qualifiers, and the transitivity or lack thereof associated with different ontology relations. We also discuss several biases that can confound aggregate analyses such as gene enrichment analyses. For each of these pitfalls and biases, we suggest remedies and best practices.

EGFR molecular testing in African-American non-small cell lung cancer patients - a review of discrepant data

Substantial discrepancy in the literature has recently emerged regarding epidermal growth factor receptor (EGFR) mutational frequency in African American (AA) NSCLC. The first wave of tissue profiling studies, including by our group in 2009, consistently observed a significantly lower frequency of EGFR mutation in AA vs. White NSCLC, whereas three recent reports appear to directly contradict these findings. Reasons for this discrepancy are unclear, but one plausible explanation arises from Simpson's paradox, the consequence of aggregating heterogeneous study cohorts (in this case, the proportion of never-smokers in the study cohort). Our review of all prior studies (combined total 386 AA NSCLC cases) underscores the wide variation in the proportion of AA never-smokers among various studies (13-57%), calling inter-study comparisons into question. In parallel, we assessed objective response by RECIST to EGFR targeted therapy for AA NSCLC in the community setting, prior to the advent of routine EGFR testing. We observed a trend toward reduced response for community-based treatment of unselected AA NSCLC (5%; 3/57) as compared to overall response rates of 10% reported by large North American trials of primarily White NSCLC patients, but this was not significant (P=0.223).

Simpson's paradox - aggregating and partitioning populations in health disparities of lung cancer patients

It is well known that non-small cell lung cancer (NSCLC) is a heterogeneous group of diseases. Previous studies have demonstrated genetic variation among different ethnic groups in the epidermal growth factor receptor (EGFR) in NSCLC. Research by our group and others has recently shown a lower frequency of EGFR mutations in African Americans with NSCLC, as compared to their White counterparts. In this study, we use our original study data of EGFR pathway genetics in African American NSCLC as an example to illustrate that univariate analyses based on aggregation versus partition of data leads to contradictory results, in order to emphasize the importance of controlling statistical confounding. We further investigate analytic approaches in logistic regression for data with separation, as is the case in our example data set, and apply appropriate methods to identify predictors of EGFR mutation. Our simulation shows that with separated or nearly separated data, penalized maximum likelihood (PML) produces estimates with smallest bias and approximately maintains the nominal value with statistical power equal to or better than that from maximum likelihood and exact conditional likelihood methods. Application of the PML method in our example data set shows that race and EGFR-FISH are independently significant predictors of EGFR mutation.