scholarly journals ANALYZING MICROARRAY DATA WITH TRANSITIVE DIRECTED ACYCLIC GRAPHS

2009 ◽  
Vol 07 (01) ◽  
pp. 135-156 ◽  
Author(s):  
VINHTHUY PHAN ◽  
E. OLUSEGUN GEORGE ◽  
QUYNH T. TRAN ◽  
SHIRLEAN GOODWIN ◽  
SRIDEVI BODREDDIGARI ◽  
...  

Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treatment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful in clustering treatment effects, annotating existing clustering methods, and analyzing sample sizes. Advantages of this approach include: (1) unique and descriptive meaning of each cluster in terms of how genes respond to all pairs of treatments; (2) insensitivity of the observed patterns to the number of genes analyzed; and (3) a combinatorial perspective to address the sample size problem by observing the rate of contractible tDAG as the number of replicates increases. The advantages and overall utility of the method in elaborating drug structure activity relationships are exemplified in a controlled study with real and simulated data.

2020 ◽  
Vol 17 (167) ◽  
pp. 20190675
Author(s):  
Joshua Havumaki ◽  
Marisa C. Eisenberg

Accurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG-derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g. reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.


2019 ◽  
Author(s):  
Joshua Havumaki ◽  
Marisa C. Eisenberg

1AbstractAccurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG–derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g., reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.


2017 ◽  
Author(s):  
Ramon Diaz-Uriarte

AbstractThe identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to try to identify these constraints, and return Directed Acyclic Graphs (DAGs) of genes. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes —e.g., those with reciprocal sign epistasis— cannot be represented by CPMs. Using simulated data under 500 fitness landscapes, I show that CPMs’ performance (prediction of genotypes that can exist) degrades with reciprocal sign epistasis. There is large variability in the DAGs inferred from each landscape, which is also affected by mutation rate, detection regime, and fitness landscape features, in ways that depend on CPM method. And the same DAG is often observed in very different landscapes, which differ in more than 50% of their accessible genotypes. Using a pancreatic data set, I show that this many-to-many relationship affects the analysis of empirical data. Fitness landscapes that are widely different from each other can, when evolutionary processes run repeatedly on them, both produce data similar to the empirically observed one, and lead to DAGs that are very different among themselves. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs.


2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S123-S123
Author(s):  
Kimberly Vanover ◽  
Robert Davis ◽  
Suresh Durgam ◽  
Jason Huo ◽  
Sharon Mates ◽  
...  

Abstract Background Deficits in social functioning are a core feature of schizophrenia and may be due to the interaction of multiple factors including negative symptoms, depression symptoms, and deficits in social cognition. Social and functional impairment in schizophrenia can be difficult to treat, may not correlate with improvements in psychotic symptoms, and has been associated with poor long-term patient outcomes. Lumateperone (lumateperone tosylate, ITI-007) is a mechanistically novel agent for the treatment of schizophrenia that simultaneously modulates serotonin, dopamine, and glutamate neurotransmission. Lumateperone was shown to be efficacious and well tolerated in 2 acute placebo-controlled studies and its safety and effectiveness was further supported in a long-term open-label study. The effects of lumateperone 42 mg (ITI-007 60 mg) on schizophrenia symptoms associated with social function were investigated in a post hoc analysis of a study that included risperidone 4 mg as an active control (Study 005, NCT01499563). Symptoms associated with social functioning were assessed with the Positive and Negative Syndrome Scale (PANSS)-derived Prosocial factor (PANSS items P3, P6, N2, N4, N7, G16), which has been utilized previously to evaluate the efficacy of various antipsychotics on this functional domain. Methods This is a post hoc analysis of data from a positive placebo- and active-controlled study in patients with an acute exacerbation of schizophrenia. Change from baseline in the PANSS Prosocial factor was assessed in the intent-to-treat (ITT) population and in patients with prominent negative symptoms (PNS, score ≥4 on at least 3 negative symptom items) or moderate-to-severe depression symptoms (Calgary Depression Scale for Schizophrenia [CDSS] ≥6) at baseline. Inferential analysis was conducted using a mixed-effects model for repeated measures (MMRM). Results The ITT population comprised 231 patients (placebo, n=80; lumateperone 42 mg, n=76; risperidone 4 mg, n=75); the PNS and CDSS ≥6 populations comprised 110 and 54 patients, respectively. Lumateperone 42-mg treatment was associated with significantly greater improvement compared with placebo on the PANSS Prosocial factor (least-squares mean difference [LSMD] vs placebo = −2.7; P<.001). Risperidone also was superior to placebo on the PANSS Prosocial factor (LSMD= −1.8; P=.011). Similar treatment effects for lumateperone 42 mg were seen on the PANSS Prosocial factor in patients with PNS at baseline (LSMD −2.6, P=.006). Conversely, in patients with PNS, risperidone treatment showed small and non-significant treatment effects on the PANSS Prosocial factor (LSMD= −0.4; P=.707). In patients with moderate-to-severe depression symptoms at baseline, marked and significant improvements on the PANSS Prosocial factor were seen in lumateperone-treated patients (LSMD= −4.9; P<.001) but not in risperidone-treated patients (LSMD=−1.3; P=.397). Discussion Lumateperone 42 mg significantly improved schizophrenia symptoms related to social functioning. In contrast to risperidone, lumateperone was associated with similar or greater treatment effects on the PANSS Prosocial factor in patients with prominent negative symptoms or moderate-to-severe depression symptoms at baseline. These results suggest that lumateperone may have benefits on schizophrenia symptoms associated with social function.


Author(s):  
Roy F Chemaly ◽  
Francisco M Marty ◽  
Cameron R Wolfe ◽  
Steven J Lawrence ◽  
Sanjeet Dadwal ◽  
...  

Abstract Background There are no antiviral therapies for parainfluenza virus (PIV) infections. DAS181, a sialidase fusion protein, has demonstrated activity in in vitro and in animal models of PIV. Methods Adult immunocompromised patients diagnosed with PIV lower respiratory tract infection (LRTI) who required oxygen supplementation were randomized 2:1 to nebulized DAS181 (4.5 mg/day) or matching placebo for up to 10 days. Randomization was stratified by need for mechanical ventilation (MV) or supplemental oxygen (SO). The primary endpoint was the proportion of patients reaching clinical stability survival (CSS) defined as returning to room air (RTRA), normalization of vital signs for at least 24 hours, and survival up to day 45 from enrollment. Results A total of 111 patients were randomized to DAS181 (n = 74) or placebo (n = 37). CSS was achieved by 45.0% DAS181-treated patients in the SO stratum compared with 31.0% for placebo (P = .15), whereas patients on MV had no benefit from DAS181. The proportion of patients achieving RTRA was numerically higher for SO stratum DAS181 patients (51.7%) compared with placebo (34.5%) at day 28 (P = .17). In a post hoc analysis of solid organ transplant, hematopoietic cell transplantation within 1 year, or chemotherapy within 1 year, more SO stratum patients achieved RTRA on DAS181 (51.8%) compared with placebo (15.8%) by day 28 (P = .012). Conclusions The primary endpoint was not met, but post hoc analysis of the RTRA component suggests DAS181 may have clinical activity in improving oxygenation in select severely immunocompromised patients with PIV LRTI who are not on mechanical ventilation. Clinical Trials Registration. NCT01644877.


2019 ◽  
Vol 91 ◽  
pp. 78-87 ◽  
Author(s):  
Anna E. Austin ◽  
Tania A. Desrosiers ◽  
Meghan E. Shanahan

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