16The introduction of several differential gene expression analysis tools has made it difficult for 17 researchers to settle on a particular tool for RNA-seq analysis. This coupled with the appropriate 18 determination of biological replicates to give an optimum representation of the study population 19 and make biological sense. To address these challenges, we performed a survey of 8 tools used 20 for differential expression in RNA-seq analysis. We simulated 39 different datasets (from 10 to 21 200 replicates, at an interval of 5) using compcodeR with a maximum of 100 replicates. Our goal 22 was to determine the effect of varying the number of replicates on the performance (F1-score, 23 recall and precision) of the tools. EBSeq and edgeR-glmRT recorded the highest (0.9385) and 24 lowest (0.6505) average F1-score across all replicates, respectively. We also performed a 25 pairwise comparison of all the tools to determine their concordance with each other in 26 identifying differentially expressed genes. We found the greatest concordance to be between 27 limma voom treat and limma voom ebayes. Finally, we recommend employing edgeR-glmRT for 28 RNA-seq experiments involving 10-50 replicates and edgeR-glmQLF for studies with 55 to 200 29 replicates. 30Author summary 31 Downstream analysis of RNA-seq data in R often poses several challenges to researchers as it is 32 a daunting task to choose a specific differential expression analysis tool over another. 33Researchers also find it challenging to determine the number (replicates) of samples to use in 34 order to give comparable and accurate results. In this paper, we surveyed eight differential 35 expression analysis tools using different number of replicates of simulated RNA-seq count data. 36 We measured the performance of each tool and based on the recorded F1-scores, recall and 37 precision, we made the following recommendations; consider edgeR-glmRT and edgeR-glmQLF 38 for replicates of 10-50 and 55-200 respectively. 39 40 42 exponential increase in RNA-seq data generation with an equivalent rise in the development of 43 algorithms for differential gene expression (DGE) analyses with varying performances. These 44 methods seek to make data analyses relatively easier and address complex biological questions 45 with greater levels of statistical confidence. However, the challenge still remains the selection of 46 optimal DGE tools and sample size calculations for optimal accuracy. This makes the selection 47 of tools and sample sizes for optimum analyses a very crucial but daunting task. 48Over the years, several research articles have been published that address the lack of consensus 49 among DGE tools. Examples of these are the works of Seyednasrollah et al (1) who performed a 50 systematic comparison of some popular DGE tools and provided recommendations for choosing 51 the optimal tool. Rapaport et al (2) assessed a number of tools based on the performance of 52 normalization, false-positive rates and the effect of sequencing depth and sample ...
Meningitis is an inflammation of the meninges, which covers the brain and spinal cord. Every year, most individuals within sub-Saharan Africa suffer from meningococcal meningitis. Moreover, tens of thousands of these cases result in death, especially during major epidemics. The transmission dynamics of the disease keep changing, according to health practitioners. The goal of this study is to exploit robust mechanisms to manage and prevent the disease at a minimal cost due to its public health implications. A significant concern found to aid in the transmission of meningitis disease is the movement and interaction of individuals from low-risk to high-risk zones during the outbreak season. Thus, this article develops a mathematical model that ascertains the dynamics involved in meningitis transmissions by partitioning individuals into low- and high-risk susceptible groups. After computing the basic reproduction number, the model is shown to exhibit a unique local asymptotically stability at the meningitis-free equilibrium E † , when the effective reproduction number R 0 < 1 , and the existence of two endemic equilibria for which R 0 † < R 0 < 1 and exhibits the phenomenon of backward bifurcation, which shows the difficulty of relying only on the reproduction number to control the disease. The effective reproductive number estimated in real time using the exponential growth method affirmed that the number of secondary meningitis infections will continue to increase without any intervention or policies. To find the best strategy for minimizing the number of carriers and infected individuals, we reformulated the model into an optimal control model using Pontryagin’s maximum principles with intervention measures such as vaccination, treatment, and personal protection. Although Ghana’s most preferred meningitis intervention method is via treatment, the model’s simulations demonstrated that the best strategy to control meningitis is to combine vaccination with treatment. But the cost-effectiveness analysis results show that vaccination and treatment are among the most expensive measures to implement. For that reason, personal protection which is the most cost-effective measure needs to be encouraged, especially among individuals migrating from low- to high-risk meningitis belts.
Background: Current studies show early interventions of autism increase significant long-term positive effects, symptoms and, later skills. Currently, These interventions are based on the use of an early diagnostic test. Existing methods for diagnosing Autism Spectrum Disorders (ASDs) such as cognitive tests, Intelligence Quotient, and standardized tests like the Autism Diagnostic Observation Schedule (ADOS) are functionally limited since they rely on child development for diagnoses. The standard is that a child must be at least three(3) years to undergo these tests. Accurate diagnosis is only possible after this period, and this may contribute to delayed diagnosis with an overall effect on the health system. In this era of increasing genetic data, it is possible to infer the genetic patterns of the disorder. This study introduces a novel and rigorous approach for predicting ASDs in neonates and their subsequent severity by identifying significant genes that contribute to the disorder. Methods: We used a wavelet transform and t-test to identify the significant genes that contribute to the disease. We subsequently employed the Naive Bayes classifier in the prediction of the autistic status of the neonate. Additionally, Principal Component Analysis (PCA) was employed to remove all the dependencies among the genes to enhance classification. Finally, we used the K-means clustering method to predict the severity level of the disease in the neonate. Results: Up to 200 differentially expressed genes were identified and used for predicting the ASD status of the child with a classification accuracy of 95.91%. Also, the results of the K-means demonstrated that the higher the mean of the cluster, the more severe the disease would be among that corresponding group. Optimizing and implementing these models in clinical settings may significantly reduce the health burden of ASDs.
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