scholarly journals Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

2021 ◽  
Author(s):  
Aurora Savino ◽  
Niccolò de Marzo ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Statistical Power ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Breast Tumors ◽  
Small Sample ◽  
Bulk Analysis ◽  
Small Sample Sizes ◽  
Microarray Datasets ◽  
User Friendly ◽  
Cellular Components

Background: transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, samples’ heterogeneity and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicate the interpretation of bulk transcriptomic profiles. Methods: we collected 48 microarray datasets of laser capture microdissected breast tumors, and performed a meta-analysis to identify robust lists of genes differentially expressed in these tumors. We created a database with carefully harmonized metadata to be used as a resource for the research community. Results: combining the results of multiple datasets improved the statistical power, and the analysis of stroma and epithelium separately allows identifying genes with different contribution in each compartment. Conclusions: our database can profitably help biomarkers’ discovery and is readily accessible through a user-friendly web interface (https://aurorasavino.shinyapps.io/metalcm/).

scholarly journals Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3371
Author(s):  
Aurora Savino ◽  
Niccolò De Marzo ◽  
Paolo Provero ◽  
Valeria Poli
Keyword(s):  
Statistical Power ◽  
Molecular Mechanisms ◽  
Meta Analysis ◽  
Breast Tumors ◽  
Small Sample ◽  
Separate Analysis ◽  
Bulk Analysis ◽  
Small Sample Sizes ◽  
Microarray Datasets ◽  
Cellular Components

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

The Identity Status Continuum Revisited

2018 ◽  
Vol 23 (4) ◽  
pp. 289-299 ◽  
Author(s):  
Wim Meeus
Keyword(s):  
Identity Development ◽  
Identity Formation ◽  
Statistical Power ◽  
Developmental Psychology ◽  
Meta Analysis ◽  
Small Sample ◽  
Identity Status ◽  
Identity Statuses ◽  
Identity Maintenance ◽  
Small Sample Sizes

Abstract. The developmental continuum of identity status has been a topic of theoretical debate since the early 1980’s. A recent meta-analysis and recent studies with dual cycle models lead to two conclusions: (1) during adolescence there is systematic identity maturation; (2) there are two continuums of identity status progression. Both continuums show that in general adolescents move from transient identity statuses to identity statuses that mark the relative endpoints of development: from diffusion to closure, and from searching moratorium and moratorium to closure and achievement. This pattern can be framed as development from identity formation to identity maintenance. In Identity Status Interview research using Marcia’s model, not the slightest indication for a continuum of identity development was found. This may be due to the small sample sizes of the various studies leading to small statistical power to detect differences in identity status transitions, as well as developmental inconsistencies in Marcia’s model. Findings from this review are interpreted in terms of life-span developmental psychology.

scholarly journals Changing the logic of replication: A case from infant studies

2019 ◽  
Author(s):  
Francesco Margoni ◽  
Martin Shepperd
Keyword(s):  
Statistical Power ◽  
Sampling Error ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Small Sample ◽  
Infant Research ◽  
Replication Studies ◽  
Wide Range ◽  
Small Sample Sizes ◽  
Meta Analyses

Infant research is making considerable progresses. However, among infant researchers there is growing concern regarding the widespread habit of undertaking studies that have small sample sizes and employ tests with low statistical power (to detect a wide range of possible effects). For many researchers, issues of confidence may be partially resolved by relying on replications. Here, we bring further evidence that the classical logic of confirmation, according to which the result of a replication study confirms the original finding when it reaches statistical significance, could be usefully abandoned. With real examples taken from the infant literature and Monte Carlo simulations, we show that a very wide range of possible replication results would in a formal statistical sense constitute confirmation as they can be explained simply due to sampling error. Thus, often no useful conclusion can be derived from a single or small number of replication studies. We suggest that, in order to accumulate and generate new knowledge, the dichotomous view of replication as confirmatory/disconfirmatory can be replaced by an approach that emphasizes the estimation of effect sizes via meta-analysis. Moreover, we discuss possible solutions for reducing problems affecting the validity of conclusions drawn from meta-analyses in infant research.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

Clinical outcomes of cancer patients with COVID-19: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18600-e18600
Author(s):  
Maryam Alasfour ◽  
Salman Alawadi ◽  
Malak AlMojel ◽  
Philippos Apolinario Costa ◽  
Priscila Barreto Coelho ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Cancer Patients ◽  
Clinical Outcomes ◽  
Meta Analysis ◽  
Small Sample ◽  
Prediction Intervals ◽  
Invasive Ventilation ◽  
Newcastle Ottawa Scale ◽  
Small Sample Sizes ◽  
Meta Analyses

e18600 Background: Patients with coronavirus disease 2019 (COVID-19) and cancer have worse clinical outcomes compared to those without cancer. Primary studies have examined this population, but most had small sample sizes and conflicting results. Prior meta-analyses exclude most US and European data or only examine mortality. The present meta-analysis evaluates the prevalence of several clinical outcomes in cancer patients with COVID-19, including new emerging data from Europe and the US. Methods: A systematic search of PubMED, medRxiv, JMIR and Embase by two independent investigators included peer-reviewed papers and preprints up to July 8, 2020. The primary outcome was mortality. Other outcomes were ICU and non-ICU admission, mild, moderate and severe complications, ARDS, invasive ventilation, stable, and clinically improved rates. Study quality was assessed through the Newcastle–Ottawa scale. Random effects model was used to derive prevalence rates, their 95% confidence intervals (CI) and 95% prediction intervals (PI). Results: Thirty-four studies (N = 4,371) were included in the analysis. The mortality prevalence rate was 25.2% (95% CI: 21.1–29.7; 95% PI: 9.8-51.1; I 2 = 85.4), with 11.9% ICU admissions (95% CI: 9.2-15.4; 95% PI: 4.3-28.9; I 2= 77.8) and 25.2% clinically stable (95% CI: 21.1-29.7; 95% PI: 9.8-51.1; I 2 = 85.4). Furthermore, 42.5% developed severe complications (95% CI: 30.4-55.7; 95% PI: 8.2-85.9; I 2 = 94.3), with 22.7% developing ARDS (95% CI: 15.4-32.2; 95% PI: 5.8-58.6; I 2 = 82.4), and 11.3% needing invasive ventilation (95% CI: 6.7-18.4; 95% PI: 2.3-41.1; I 2 = 79.8). Post-follow up, 49% clinically improved (95% CI: 35.6-62.6; 95% PI: 9.8-89.4; I 2 = 92.5). All outcomes had large I 2 , suggesting high levels of heterogeneity among studies, and wide PIs indicating high variability within outcomes. Despite this variability, the mortality rate in cancer patients with COVID-19, even at the lower end of the PI (9.8%), is higher than the 2% mortality rate of the non-cancer with COVID-19 population, but not as high as what other meta-analyses conclude, which is around 25%. Conclusions: Patients with cancer who develop COVID-19 have a higher probability of mortality compared to the general population with COVID-19, but possibly not as high as previous studies have shown. A large proportion of them developed severe complications, but a larger proportion recovered. Prevalence of mortality and other outcomes published in prior meta-analyses did not report prediction intervals, which compromises the clinical utilization of such results.

Distinguishing autism from co-existing conditions: a behavioural profiling investigation

2016 ◽  
Vol 2 (1) ◽  
pp. 41-54
Author(s):  
Ashleigh Saunders ◽  
Karen E. Waldie
Keyword(s):  
Statistical Power ◽  
Psychiatric Comorbidity ◽  
Adaptive Functioning ◽  
Autism Spectrum ◽  
Small Sample ◽  
Comorbid Conditions ◽  
Sample Sizes ◽  
Content Type ◽  
Small Sample Sizes ◽  
Comorbid Adhd

Purpose – Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition for which there is no known cure. The rate of psychiatric comorbidity in autism is extremely high, which raises questions about the nature of the co-occurring symptoms. It is unclear whether these additional conditions are true comorbid conditions, or can simply be accounted for through the ASD diagnosis. The paper aims to discuss this issue. Design/methodology/approach – A number of questionnaires and a computer-based task were used in the current study. The authors asked the participants about symptoms of ASD, attention deficit hyperactivity disorder (ADHD) and anxiety, as well as overall adaptive functioning. Findings – The results demonstrate that each condition, in its pure form, can be clearly differentiated from one another (and from neurotypical controls). Further analyses revealed that when ASD occurs together with anxiety, anxiety appears to be a separate condition. In contrast, there is no clear behavioural profile for when ASD and ADHD co-occur. Research limitations/implications – First, due to small sample sizes, some analyses performed were targeted to specific groups (i.e. comparing ADHD, ASD to comorbid ADHD+ASD). Larger sample sizes would have given the statistical power to perform a full scale comparative analysis of all experimental groups when split by their comorbid conditions. Second, males were over-represented in the ASD group and females were over-represented in the anxiety group, due to the uneven gender balance in the prevalence of these conditions. Lastly, the main profiling techniques used were questionnaires. Clinical interviews would have been preferable, as they give a more objective account of behavioural difficulties. Practical implications – The rate of psychiatric comorbidity in autism is extremely high, which raises questions about the nature of the co-occurring symptoms. It is unclear whether these additional conditions are true comorbid conditions, or can simply be accounted for through the ASD diagnosis. Social implications – This information will be important, not only to healthcare practitioners when administering a diagnosis, but also to therapists who need to apply evidence-based treatment to comorbid and stand-alone conditions. Originality/value – This study is the first to investigate the nature of co-existing conditions in ASD in a New Zealand population.

scholarly journals Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

2019 ◽  
Author(s):  
Tianye Jia ◽  
Congying Chu ◽  
Yun Liu ◽  
Jenny van Dongen ◽  
Evangelos Papastergios ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Nucleus Accumbens ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Meta Analysis ◽  
Hippocampal Volume ◽  
Small Sample ◽  
Small Sample Sizes ◽  
Meta Analyses

AbstractDNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

Prevalence of tuberculosis, brucellosis and trypanosomiasis in cattle in Tanzania: a systematic review and meta-analysis

2016 ◽  
Vol 17 (1) ◽  
pp. 16-27 ◽  
Author(s):  
Silvia Alonso ◽  
Ian Dohoo ◽  
Johanna Lindahl ◽  
Cristobal Verdugo ◽  
Isaiah Akuku ◽  
...  
Keyword(s):  
Systematic Review ◽  
Production Systems ◽  
Strong Predictor ◽  
Meta Analysis ◽  
Small Sample ◽  
Skin Tests ◽  
Prevalence Studies ◽  
Prevalence Estimates ◽  
Using Data ◽  
Small Sample Sizes

AbstractA meta-analysis was performed to derive prevalence estimates for Brucella spp., Mycobacterium spp. and Trypanosoma spp. in cattle in Tanzania using data derived from a systematic review of zoonotic hazards in cattle production systems. Articles published before 2012 reporting prevalence and considered at least moderate in quality were included in the analysis. Results showed high heterogeneity between studies, with wide ranges in the reported prevalence: Brucella (0.3–60.8%), Mycobacterium (0.1–13.2%) and Trypanosoma (0.82–33.3%). Overall meta-analytic mean prevalence estimates were 8.2% (95% CI 6.5–10.2), 1.28% (95% CI 0.35–4.58) and 10.3% (95% CI 6.20–16.70) respectively, for Brucella spp., Mycobacterium spp. and Trypanosoma spp. Time and region were predictors of variability of Brucella spp. prevalence, while diagnostic test was a strong predictor of Mycobacterium spp. prevalence, with higher prevalence estimates given by skin tests compared with post-mortem inspection. None of the studied factors were associated with prevalence of Trypanosoma spp. The small sample sizes, range of study locations, study designs and diagnostics used, contributed to high variability among prevalence estimates. Larger and more robust prevalence studies are needed to adequately support risk assessment and management of animal and public health threats.

scholarly journals Looking for crumbs in the obesity forest: anti-obesity interventions in the Mexican population. History, and systematic review with Meta-Analysis

2021 ◽  
Author(s):  
Esperanza M. Garcia-Oropesa ◽  
Yoscelina E. Martinez-Lopez ◽  
Sonia Maria Ruiz-Cejudo ◽  
Jose Dario Martinez-Ezquerro ◽  
Alvaro Diaz-Badillo ◽  
...  
Keyword(s):  
Systematic Review ◽  
Blood Pressure ◽  
Waist Circumference ◽  
Behavioral Interventions ◽  
Meta Analysis ◽  
Small Sample ◽  
Population History ◽  
Sample Sizes ◽  
Obesity And Diabetes ◽  
Small Sample Sizes

Mexicans and Mexican Americans share culture, genetic background, and predisposition for chronic complications associated with obesity and diabetes making imperative efficacious treatments and prevention. Obesity has been treated for centuries focused-on weight loss while other treatments on associated conditions like gout, diabetes (T2D), and hypertriglyceridemia. To date, there is no systematic review that synthetize the origin of obesity clinics in Mexico and the efforts to investigate treatments for obesity tested by randomized clinical trials (RCT). We conducted systematic searches in Pubmed, Scopus, and Web of Science to retrieve anti-obesity RCT through 2019 and without inferior temporal limit. The systematic review included RCT of anti-obesity treatments in the Mexican adult population, including alternative medicine, pharmacological, nutritional, behavioral, and surgical interventions reporting biometric outcomes such as BMI, weight, waist circumference, triglycerides, glucose, among others. Studies with at least three months of treatment were included in the meta-analysis. We found 634 entries, after removal of duplicates and screening the studies based on eligibility criteria, we analyzed 43, and 2 multinational-collaborative studies. Most of the national studies have small sample sizes, and the studied strategies do not have replications in the population. The nutrition/behavioral interventions were difficult to blind, and most studies have medium to high risk of bias. Nutritional/behavioral interventions and medications showed effects on BMI, waist circumference, and blood pressure. Simple measures like plain water instead of sweet beverages decrease triglycerides and systolic blood pressure. Participants with obesity and hypertension can have benefic effects with antioxidants, and treatment with insulin increase weight in those with T2D. The study of obesity in Mexico has been on-going for more than four decades, but the interest on RCT just increased until this millennium, but with small sample sizes and lack of replication. The interventions affect different metabolic syndrome components, which should be analyzed in detail with the population living on the U.S.-Mexico border; therefore, bi-national collaboration is desirable to disentangle the cultural effects on this population's treatment response.

scholarly journals Meta-analysis of COVID-19 single-cell studies confirms eight key immune responses

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Manik Garg ◽  
Xu Li ◽  
Pablo Moreno ◽  
Irene Papatheodorou ◽  
Yuelong Shu ◽  
...  
Keyword(s):  
Immune Responses ◽  
Single Cell ◽  
Meta Analysis ◽  
Small Sample ◽  
Signal Pathways ◽  
Type I ◽  
Multiple Datasets ◽  
Cell Clones ◽  
Small Sample Sizes ◽  
Cell Clonal Expansion

AbstractSeveral single-cell RNA sequencing (scRNA-seq) studies analyzing immune response to COVID-19 infection have been recently published. Most of these studies have small sample sizes, which limits the conclusions that can be made with high confidence. By re-analyzing these data in a standardized manner, we validated 8 of the 20 published results across multiple datasets. In particular, we found a consistent decrease in T-cells with increasing COVID-19 infection severity, upregulation of type I Interferon signal pathways, presence of expanded B-cell clones in COVID-19 patients but no consistent trend in T-cell clonal expansion. Overall, our results show that the conclusions drawn from scRNA-seq data analysis of small cohorts of COVID-19 patients need to be treated with some caution.

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