scholarly journals Double reduction estimation and equilibrium tests in natural autopolyploid populations

2021 ◽  
Author(s):  
David Gerard
Keyword(s):  
Random Mating ◽  
R Package ◽  
Small Sample ◽  
Double Reduction ◽  
Bootstrap Procedure ◽  
Likelihood Approach ◽  
Small Sample Sizes ◽  
Minimization Approach ◽  
Quantities Of Interest ◽  
Do So

AbstractMany bioinformatics pipelines include tests for equilibrium. Tests for diploids are well studied and widely available, but extending these approaches to autopolyploids is hampered by the presence of double reduction, the co-migration of sister chromatid segments into the same gamete during meiosis. Though a hindrance for equilibrium tests, double reduction rates are quantities of interest in their own right, as they provide insights about the meiotic behavior of autopolyploid organisms. Here, we develop procedures to (i) test for equilibrium while accounting for double reduction, and (ii) estimate double reduction given equilibrium. To do so, we take two approaches: a likelihood approach, and a novel U-statistic minimization approach that we show generalizes the classical equilibrium χ2 test in diploids. For small sample sizes and uncertain genotypes, we further develop a bootstrap procedure based on our U-statistic to test for equilibrium. Finally, we highlight the difficulty in distinguishing between random mating and equilibrium in tetraploids at biallelic loci. Our methods are implemented in the hwep R package on GitHub https://github.com/dcgerard/hwep.

scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: an innovative R-package to visualize raw behavioral data over time

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8341
Author(s):  
Elodie M. Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals Power Analysis for Conjoint Experiments

2020 ◽  
Author(s):  
Julian Schuessler ◽  
Markus Freitag
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Small Sample ◽  
Effect Sizes ◽  
Sample Sizes ◽  
Social Scientists ◽  
Standard Tool ◽  
Sample Size Planning ◽  
Small Sample Sizes ◽  
Power Analyses

Conjoint experiments aiming to estimate average marginal component effects and related quantities have become a standard tool for social scientists. However, existing solutions for power analyses to find appropriate sample sizes for such studies have various shortcomings and accordingly, explicit sample size planning is rare. Based on recent advances in statistical inference for factorial experiments, we derive simple yet generally applicable formulae to calculate power and minimum required sample sizes for testing average marginal component effects (AMCEs), conditional AMCEs, as well as interaction effects in forced-choice conjoint experiments. The only input needed are expected effect sizes. Our approach only assumes random sampling of individuals or randomization of profiles and avoids any parametric assumption. Furthermore, we show that clustering standard errors on individuals is not necessary and does not affect power. Our results caution against designing conjoint experiments with small sample sizes, especially for detecting heterogeneity and interactions. We provide an R package that implements our approach.

scholarly journals LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

2021 ◽  
Author(s):  
Yingtian Hu ◽  
Glen Satten ◽  
Yijuan Hu
Keyword(s):  
Logistic Regression ◽  
False Discovery Rate ◽  
Compositional Analysis ◽  
R Package ◽  
Small Sample ◽  
Metagenomic Sequencing ◽  
False Discovery ◽  
Small Sample Sizes ◽  
Low Sensitivity ◽  
Microbiome Data

Abstract Motivation: Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant", while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods of compositional analysis such as ANCOM or ANCOM-BC use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren, Willis and Callahan [1] have recently proposed a model for how these biases affect relative abundance data. Methods: Motivated by [1], we show that the (log) odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose LOCOM, a robust logistic regression approach to compositional analysis, that does not require pseudocounts. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported. Results: Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally; ALDEx2 generally had low sensitivity. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC and ALDEx2 had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Availability and implementation: Our R package LOCOM is available on GitHub at https://github.com/yijuanhu/LOCOM in formats appropriate for Macintosh or Windows.

scholarly journals Significance tests for analyzing gene expression data with small sample sizes

2019 ◽  
Vol 35 (20) ◽  
pp. 3996-4003
Author(s):  
Insha Ullah ◽  
Sudhir Paul ◽  
Zhenjie Hong ◽  
You-Gan Wang
Keyword(s):  
Gene Expression ◽  
Statistical Power ◽  
Degrees Of Freedom ◽  
Lymphoblastic Leukemia ◽  
R Package ◽  
Small Sample ◽  
Supplementary Information ◽  
Equal Variance ◽  
Small Sample Sizes ◽  
T Distribution

Abstract Motivation Under two biologically different conditions, we are often interested in identifying differentially expressed genes. It is usually the case that the assumption of equal variances on the two groups is violated for many genes where a large number of them are required to be filtered or ranked. In these cases, exact tests are unavailable and the Welch’s approximate test is most reliable one. The Welch’s test involves two layers of approximations: approximating the distribution of the statistic by a t-distribution, which in turn depends on approximate degrees of freedom. This study attempts to improve upon Welch’s approximate test by avoiding one layer of approximation. Results We introduce a new distribution that generalizes the t-distribution and propose a Monte Carlo based test that uses only one layer of approximation for statistical inferences. Experimental results based on extensive simulation studies show that the Monte Carol based tests enhance the statistical power and performs better than Welch’s t-approximation, especially when the equal variance assumption is not met and the sample size of the sample with a larger variance is smaller. We analyzed two gene-expression datasets, namely the childhood acute lymphoblastic leukemia gene-expression dataset with 22 283 genes and Golden Spike dataset produced by a controlled experiment with 13 966 genes. The new test identified additional genes of interest in both datasets. Some of these genes have been proven to play important roles in medical literature. Availability and implementation R scripts and the R package mcBFtest is available in CRAN and to reproduce all reported results are available at the GitHub repository, https://github.com/iullah1980/MCTcodes. Supplementary information Supplementary data is available at Bioinformatics online.

scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals A simple model-based approach to inferring and visualizing cancer mutation signatures

2015 ◽  
Author(s):  
Yuichi Shiraishi ◽  
Georg Tremmel ◽  
Satoru Miyano ◽  
Matthew Stephens
Keyword(s):  
Web Application ◽  
Probabilistic Models ◽  
Somatic Mutations ◽  
Context Effects ◽  
R Package ◽  
Upper Urinary Tract ◽  
Small Sample ◽  
Genetic Admixture ◽  
Sequencing Technologies ◽  
Small Sample Sizes

Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or ``mutation signatures'' at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provide a new intuitive way to visualize the signatures, analogous to the use of sequence logos to visualize transcription factor binding sites. We illustrate our new method on somatic mutation data from urothelial carcinoma of the upper urinary tract, and a larger dataset from 30 diverse cancer types. The results illustrate several important features of our methods, including the ability of our new visualization tool to clearly highlight the key features of each signature, the improved robustness of signature inferences from small sample sizes, and more detailed inference of signature characteristics such as strand biases and sequence context effects at the base two positions 5' to the mutated site. The overall framework of our work is based on probabilistic models that are closely connected with ``mixed-membership models'' which are widely used in population genetic admixture analysis, and in machine learning for document clustering. We argue that recognizing these relationships should help improve understanding of mutation signature extraction problems, and suggests ways to further improve the statistical methods. Our methods are implemented in an R package pmsignature (https://github.com/friend1ws/pmsignature) and a web application available at https://friend1ws.shinyapps.io/pmsignature_shiny/.

scholarly journals LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control

2021 ◽  
Author(s):  
Yingtian Hu ◽  
Glen A. Satten ◽  
Yi-Juan Hu
Keyword(s):  
Logistic Regression ◽  
False Discovery Rate ◽  
Compositional Analysis ◽  
R Package ◽  
Small Sample ◽  
Metagenomic Sequencing ◽  
False Discovery ◽  
Small Sample Sizes ◽  
Low Sensitivity ◽  
Microbiome Data

AbstractMotivationCompositional analysis is based on the premise that a relatively small proportion of taxa are “differentially abundant”, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods of compositional analysis such as ANCOM or ANCOM-BC use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren, Willis and Callahan [1] have recently proposed a model for how these biases affect relative abundance data.MethodsMotivated by [1], we show that the (log) odds ratios in a logistic regression comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose LOCOM, a robust logistic regression approach to compositional analysis, that does not require pseudocounts. We use a Firth bias-corrected estimating function to account for sparse data. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for continuous and/or discrete confounding covariates is supported.ResultsOur simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, ANCOM often had inflated FDR; ANCOM-BC largely controlled FDR but still had modest inflation occasionally; ALDEx2 generally had low sensitivity. LOCOM and ANCOM were robust to experimental biases in every situation, while ANCOM-BC and ALDEx2 had elevated FDR when biases at causal and non-causal taxa were differentially distributed. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies.Availability and implementationOur R package LOCOM is available on GitHub at https://github.com/yijuanhu/LOCOM in formats appropriate for Macintosh or Windows.

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