scholarly journals Power Analysis Tutorial for Experimental Design Software

2014 ◽  
Author(s):  
Laura J. Freeman ◽  
Thomas H. Johnson ◽  
James R. Simpson
Keyword(s):  
Experimental Design ◽  
Power Analysis ◽  
Design Software

Analysis of Braced Excavation with Pit-in-Pit Based on Orthogonal Experiment

2011 ◽  
Vol 71-78 ◽  
pp. 4549-4553 ◽  
Author(s):  
Tian Ping Qian ◽  
Xiao Nan Gong ◽  
Ying Li
Keyword(s):  
Experimental Design ◽  
Orthogonal Experiment ◽  
Orthogonal Experimental Design ◽  
Foundation Pit ◽  
Supporting Structure ◽  
Wall Deflection ◽  
Whole Process ◽  
Design Software ◽  
Pit Depth ◽  
Significant Factors

In order to study the supporting structure under different distances between inner pit-in-pit and foundation pit, width and depth of pit-in-pit and general excavation depth of external foundation pit, PLAXIS is adopted to simulate the whole process of pit-in-pit excavation in a foundation pit of Shanghai. The factors which influence wall deflection are analyzed using orthogonal experimental design software. The results show that the factors which influence deformation of pit-in-pit and supporting structure of foundation pit are sorted with range value or sensitivity descending: depth of pit-in-pit, location of pit-in-pit, general excavation depth of external foundation pit, and width of pit-in-pit. Depth and location of pit-in-pit are the most two significant factors, and they have the strongest interactions.

scholarly journals RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment

2014 ◽  
Vol 13s6 ◽  
pp. CIN.S17688 ◽  
Author(s):  
Yan Guo ◽  
Shilin Zhao ◽  
Chung-I Li ◽  
Quanhu Sheng ◽  
Yu Shyr
Keyword(s):  
Experimental Design ◽  
Sample Size ◽  
High Throughput ◽  
Power Analysis ◽  
High Throughput Sequencing ◽  
Genome Wide Association Study ◽  
Negative Binomial ◽  
Sample Size Calculation ◽  
Power Calculation ◽  
Biological Studies

Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/ .

scholarly journals Design and power analysis for multi-sample single cell genomics experiments

2020 ◽  
Author(s):  
Katharina T. Schmid ◽  
Cristiana Cruceanu ◽  
Anika Böttcher ◽  
Heiko Lickert ◽  
Elisabeth B. Binder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Experimental Design ◽  
Single Cell ◽  
Expression Analysis ◽  
Quantitative Trait ◽  
Power Analysis ◽  
Rna Seq ◽  
Wide Range ◽  
Differential Gene ◽  
Number Of Cells

AbstractBackgroundThe identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons.ResultsHere we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power.ConclusionOur model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

scholarly journals A flexible simulation toolkit for designing and evaluating ChIP-sequencing experiments

2019 ◽  
Author(s):  
An Zheng ◽  
Michael Lamkin ◽  
Yutong Qiu ◽  
Kevin Ren ◽  
Alon Goren ◽  
...  
Keyword(s):  
Experimental Design ◽  
Statistical Models ◽  
Power Analysis ◽  
Ground Truth ◽  
Peak Calling ◽  
Ground Truth Data ◽  
Chip Sequencing ◽  
Analysis Tools ◽  
Differential Binding ◽  
On Chip

AbstractA major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. We present Tulip, a toolkit for rapidly simulating ChIP-seq data using statistical models of the experimental steps. Tulip may be used for a range of applications, including power analysis for experimental design, benchmarking of analysis tools, and modeling effects of processes such as replication on ChIP-seq signals.

A Micro-Controller Implementation of a Fialka M-125 Based Stream Cipher

2014 ◽  
Vol 60 (1) ◽  
pp. 101-116
Author(s):  
Eugen Antal ◽  
Viliam Hromada
Keyword(s):  
Performance Analysis ◽  
Personal Computer ◽  
Power Analysis ◽  
Stream Cipher ◽  
Software Implementation ◽  
Secret Key ◽  
Design Software ◽  
Encryption Decryption ◽  
And Performance ◽  
Evaluation Board

Abstract In 2013, a new stream cipher was proposed in Antal, E.-Hromada, V.: A new stream cipher based on Fialka M-125, Tatra Mt. Math. Publ. 57 (2013), 101-118. Its design was inspired and motivated by a Soviet encryption machine Fialka M-125. The authors proposed three versions of the cipher with different inner state bit-lengths. They provided the design, software implementation on a personal computer and a preliminary statistical and performance analysis of the cipher. In this article we extend their work by implementing all three versions of the cipher on two different micro-controllers: EBV SoCrates evaluation board [Official SoCrates webpage (EBV SoCrates evaluation board), www.rockerboards.org] and STM32F407VG [Official STM webpage (STM32F407VG), www.st.com]. We evaluate the performance of all implementations on both platforms. We also investigate the possibilities of performing a simple power analysis of the implementation of the 8-bit version of the cipher implemented on STM32F407VG micro-controller. It stems from our experiments that we are able to determine a part of the secret key of the cipher by observing the power trace (power consumption) of the encryption/decryption process

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

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