scholarly journals Statistical analysis of cytogenetic data

2021 ◽  
Vol 28 ◽  
pp. 146-150
Author(s):  
L. A. Atramentova

Using the data obtained in a cytogenetic study as an example, we consider the typical errors that are made when performing statistical analysis. Widespread but flawed statistical analysis inevitably produces biased results and increases the likelihood of incorrect scientific conclusions. Errors occur due to not taking into account the study design and the structure of the analyzed data. The article shows how the numerical imbalance of the data set leads to a bias in the result. Using a dataset as an example, it explains how to balance the complex. It shows the advantage of presenting sample indicators with confidence intervals instead of statistical errors. Attention is drawn to the need to take into account the size of the analyzed shares when choosing a statistical method. It shows how the same data set can be analyzed in different ways depending on the purpose of the study. The algorithm of correct statistical analysis and the form of the tabular presentation of the results are described. Keywords: data structure, numerically unbalanced complex, confidence interval.

2010 ◽  
Vol 45 (1) ◽  
pp. 98-100 ◽  
Author(s):  
Kenneth L. Knight

Abstract Context: The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand. Objective: To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs. Description: The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style. At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary. Advantages: Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.


2020 ◽  
Vol 18 (1) ◽  
pp. 2-15
Author(s):  
Jennifer E. V. Lloyd ◽  
Jacqui Boonstra ◽  
Barry Forer ◽  
Rush Hershler ◽  
Constance Milbrath ◽  
...  

Population-based, person-specific, longitudinal child and youth health and developmental data linkages involve connecting combinations of specially-collected data and administrative data for longitudinal population research purposes. This glossary provides definitions of key terms and concepts related to their theoretical basis, research infrastructure, research methodology, statistical analysis, and knowledge translation.


Author(s):  
А.А. Брусков

Надёжность долгое время признавалась главным качеством для систем космического аппарата. К сожалению, в литературе имеются лишь ограниченные данные об отказах на орбите и статистическом анализе надежности спутников. Для восполнения этого пробела был проведен непараметрический анализ надежности спутников для спутников на околоземной орбите. В этой работе я расширяю статистический анализ надежности спутников и исследую надежность подсистем космических аппаратов. Поскольку набор данных подвергнут цензуре, я широко использую оценщик Каплана-Мейера для расчета функций надежности и получаю доверительные интервалы для непараметрических результатов надежности для каждой подсистемы спутика. Reliability has long been recognized as the main quality for spacecraft systems. Unfortunately, there is only limited data in the literature on failures in orbit and statistical analysis of the reliability of satellites. To fill this gap, a nonparametric analysis of the reliability of satellites for satellites in near-Earth orbit was carried out. In this work, I expand the statistical analysis of the reliability of satellites and investigate the reliability of spacecraft subsystems. Since the data set is censored, I make extensive use of the Kaplan-Meyer estimator to calculate reliability functions and obtain confidence intervals for nonparametric reliability results for each spootik subsystem.


Filomat ◽  
2017 ◽  
Vol 31 (10) ◽  
pp. 2967-2974
Author(s):  
Vesna Rajic

We examine one-sided confidence intervals for the population variance, based on the ordinary t-statistics. We derive an unconditional coverage probability of the bootstrap-t interval for unknown variance. For that purpose, we find an Edgeworth expansion of the distribution of t-statistic to an order n-2. We can see that a number of simulation, B, has the influence on coverage probability of the confidence interval for the variance. If B equals sample size then coverage probability and its limit (when B ? ?) disagree at the level O(n-2). If we want that nominal coverage probability of the interval would be equal to ?, then coverage probability and its limit agree to order n-3/2 if B is of larger order than the square root of the sample size. We present a modeling application in insurance property, where the purpose of analysis is to measure variability of a data set.


Author(s):  
Jinsoo Park ◽  
Haneul Lee ◽  
Yun Bae Kim

In the simulation output analysis, there are some measures that should be calculated by time average concept such as the mean queue length. Especially, the confidence interval of those measures might be required for statistical analysis. In this situation, the traditional method that utilizes the central limit theorem (CLT) is inapplicable if the output data set has autocorrelation structure. The bootstrap is one of the most suitable methods which can reflect the autocorrelated phenomena in statistical analysis. Therefore, the confidence interval for a time averaged measure having autocorrelation structure can also be calculated by the bootstrap methods. This study introduces the method that constructs these confidence intervals applying the bootstraps. The bootstraps proposed are the threshold bootstrap (TB), the moving block bootstrap (MBB) and stationary bootstrap (SB). Finally, some numerical examples will be provided for verification.


Author(s):  
Sadegh Dorri Nogoorani ◽  
Rasool Jalili

Uncertainty and its imposed risk have significant impacts on decision-making. However, both are disregarded in many trust-based applications. In this paper, we propose a risk-aware approach to explicitly take uncertainty of trust and its effects into account. Our approach consists of a trust, a confidence, and a risk model. We do not prescribe a specific trust model, and any probabilistic trust model can be empowered by our approach. The confidence model calculates the uncertainty of the trust model in the form of a confidence interval, and is independent of the inner-workings of the trust model. This interval is used by the utility-based risk model which assesses the effects of uncertainty on trust-based decisions. We evaluated our approach by a four-state HMM-based simulated trustee, and employed the Beta, HMM and evidence-based trust models. We proposed and compared different methods for calculating confidence intervals, as well as methods for determining the risk and opportunity of a trust-based interaction. The results demonstrate how our approach should be used to improve the correctness of decision-making in trust-based applications. According to the statistical analysis of the simulation results, confidence intervals can properly represent the trust value and its uncertainty, and strongly improve trust-based decisions.


1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda

2020 ◽  
Author(s):  
Hideya Kawasaki ◽  
Hiromi Suzuki ◽  
Masato Maekawa ◽  
Takahiko Hariyama

BACKGROUND As pathogens such as influenza virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can easily cause pandemics, rapid diagnostic tests are crucial for implementing efficient quarantine measures, providing effective treatments to patients, and preventing or containing a pandemic infection. Here, we developed the immunochromatography-NanoSuit® method, an improved immunochromatography method combined with a conventional scanning electron microscope (SEM), which enables observation of immunocomplexes labeled with a colloidal metal. OBJECTIVE A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. METHODS Immunochromatography kit The ImunoAce® Flu kit (NP antigen detection), a human influenza commercial diagnosis kit, was purchased from TAUNS Laboratories, Inc. (Shizuoka, Japan). Au/Pt nanoparticles were utilized to visualize the positive lines. A total of 197 clinical samples from patients suspected to be suffering from influenza were provided by a general hospital at the Hamamatsu University School of Medicine for examination using the Flu kit. After macroscopic diagnosis using the Flu kit, the samples were stored in a biosafety box at room temperature (20-25 °C / 68 - 77 °F). The IgM detection immunochromatography kit against SARS-CoV-2 was obtained from Kurabo Industries, Ltd. (Osaka, Japan). One step rRT-PCR for influenza A rRT-PCR for influenza A was performed as described previously using Flu A universal primers. A Ct within 38.0 was considered as positive according to the CDC protocol. The primer/probe set targeted the human RNase P gene and served as an internal control for human nucleic acid as described previously. SEM image acquisition The immunochromatography kit was covered with a modified NanoSuit® solution based on previously published components (Nisshin EM Co., Ltd., Tokyo, Japan), placed first onto the wide stage of the specimen holder, and then placed in an Lv-SEM (TM4000Plus, Hitachi High-Technologies, Tokyo, Japan). Images were acquired using backscattered electron detectors with 10 or 15 kV at 30 Pa. Particle counting In fields containing fewer than 50 particles/field, the particles were counted manually. Otherwise, ImageJ/Fiji software was used for counting. ImageJ/Fiji uses comprehensive particle analysis algorithms that effectively count various particles. Images were then processed and counting was performed according to the protocol. Diagnosis and statistics The EM diagnosis and criteria for a positive test were defined as follows: particle numbers from 6 fields from the background area and test-line were statistically analyzed using the t-test. If there were more than 5 particles in one visual field and a significant difference (P < 0.01) was indicated by the t-test, the result was considered positive. Statistical analysis using the t-test was performed in Excel software. Statistical analysis of the assay sensitivity and specificity with a 95% confidence interval (95% CI) was performed using the MedCalc statistical website. The approximate line, correlation coefficient, and null hypothesis were calculated with Excel software. RESULTS Our new immunochromatography-NanoSuit® method suppresses cellulose deformity and makes it possible to easily focus and acquire high-resolution images of gold/platinum labeled immunocomplexes of viruses such as influenza A, without the need for conductive treatment as with conventional SEM. Electron microscopy (EM)-based diagnosis of influenza A exhibited 94% clinical sensitivity (29/31) (95% confidence interval [95%CI]: 78.58–99.21%) and 100% clinical specificity (95%CI: 97.80–100%). EM-based diagnosis was significantly more sensitive (71.2%) than macroscopic diagnosis (14.3%), especially in the lower influenza A-RNA copy number group. The detection ability of our method is comparable to that of real-time reverse transcription-polymerase chain reaction. CONCLUSIONS This simple and highly sensitive quantitative analysis method involving immunochromatography can be utilized to diagnose various infections in humans and livestock, including highly infectious diseases such as COVID-19.


Genetics ◽  
1998 ◽  
Vol 148 (1) ◽  
pp. 525-535
Author(s):  
Claude M Lebreton ◽  
Peter M Visscher

AbstractSeveral nonparametric bootstrap methods are tested to obtain better confidence intervals for the quantitative trait loci (QTL) positions, i.e., with minimal width and unbiased coverage probability. Two selective resampling schemes are proposed as a means of conditioning the bootstrap on the number of genetic factors in our model inferred from the original data. The selection is based on criteria related to the estimated number of genetic factors, and only the retained bootstrapped samples will contribute a value to the empirically estimated distribution of the QTL position estimate. These schemes are compared with a nonselective scheme across a range of simple configurations of one QTL on a one-chromosome genome. In particular, the effect of the chromosome length and the relative position of the QTL are examined for a given experimental power, which determines the confidence interval size. With the test protocol used, it appears that the selective resampling schemes are either unbiased or least biased when the QTL is situated near the middle of the chromosome. When the QTL is closer to one end, the likelihood curve of its position along the chromosome becomes truncated, and the nonselective scheme then performs better inasmuch as the percentage of estimated confidence intervals that actually contain the real QTL's position is closer to expectation. The nonselective method, however, produces larger confidence intervals. Hence, we advocate use of the selective methods, regardless of the QTL position along the chromosome (to reduce confidence interval sizes), but we leave the problem open as to how the method should be altered to take into account the bias of the original estimate of the QTL's position.


2020 ◽  
Vol 499 (4) ◽  
pp. 5641-5652
Author(s):  
Georgios Vernardos ◽  
Grigorios Tsagkatakis ◽  
Yannis Pantazis

ABSTRACT Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.


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