scholarly journals pyAffy: An efficient Python/Cython implementation of the RMA method for processing raw data from Affymetrix expression microarrays

2016 ◽  
Author(s):  
Florian Wagner
Keyword(s):  
Microarray Data ◽  
Source Code ◽  
R Package ◽  
Raw Data ◽  
Maqc Project ◽  
Reference Implementation ◽  
Using Data ◽  
Expression Microarrays ◽  
Python Package ◽  
Final Expression

Robust multi-array average (RMA) is a highly successful method for processing raw data from Affymetrix expression microarrays. However, most of the work on microarray data processing predates the widespread use of Python in scientific computing. Here, I describe pyAffy, an efficient implementation of the RMA method in Python/Cython. Using data from the MAQC project, I show that this implementation produces virtually identical results compared to the RMA reference implementation in the affy R package, while running more than five times faster and consuming significantly less memory. I also show how individual steps of the RMA method affect the final expression estimates. The source code for pyAffy is available from PyPI and GitHub (https://github.com/flo-compbio/pyaffy) under an OSI-approved license. I intend to periodically revise this article to ensure that it accurately reflects the functionalities available in the pyAffy Python package.

scholarly journals pyAffy: An efficient Python/Cython implementation of the RMA method for processing raw data from Affymetrix expression microarrays

2016 ◽  
Author(s):  
Florian Wagner
Keyword(s):  
Microarray Data ◽  
Source Code ◽  
R Package ◽  
Raw Data ◽  
Maqc Project ◽  
Reference Implementation ◽  
Using Data ◽  
Expression Microarrays ◽  
Python Package ◽  
Final Expression

Robust multi-array average (RMA) is a highly successful method for processing raw data from Affymetrix expression microarrays. However, most of the work on microarray data processing predates the widespread use of Python in scientific computing. Here, I describe pyAffy, an efficient implementation of the RMA method in Python/Cython. Using data from the MAQC project, I show that this implementation produces virtually identical results compared to the RMA reference implementation in the affy R package, while running more than five times faster and consuming significantly less memory. I also show how individual steps of the RMA method affect the final expression estimates. The source code for pyAffy is available from PyPI and GitHub (https://github.com/flo-compbio/pyaffy) under an OSI-approved license. I intend to periodically revise this article to ensure that it accurately reflects the functionalities available in the pyAffy Python package.

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals WORCS: A workflow for open reproducible code in science

Data Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Caspar J. Van Lissa ◽  
Andreas M. Brandmaier ◽  
Loek Brinkman ◽  
Anna-Lena Lamprecht ◽  
Aaron Peikert ◽  
...  
Keyword(s):  
Best Practices ◽  
Source Code ◽  
R Package ◽  
Open Science ◽  
Research Projects ◽  
Tabular Data ◽  
Step Procedure ◽  
Starting Point ◽  
Conducting Research ◽  
And Training

Adopting open science principles can be challenging, requiring conceptual education and training in the use of new tools. This paper introduces the Workflow for Open Reproducible Code in Science (WORCS): A step-by-step procedure that researchers can follow to make a research project open and reproducible. This workflow intends to lower the threshold for adoption of open science principles. It is based on established best practices, and can be used either in parallel to, or in absence of, top-down requirements by journals, institutions, and funding bodies. To facilitate widespread adoption, the WORCS principles have been implemented in the R package worcs, which offers an RStudio project template and utility functions for specific workflow steps. This paper introduces the conceptual workflow, discusses how it meets different standards for open science, and addresses the functionality provided by the R implementation, worcs. This paper is primarily targeted towards scholars conducting research projects in R, conducting research that involves academic prose, analysis code, and tabular data. However, the workflow is flexible enough to accommodate other scenarios, and offers a starting point for customized solutions. The source code for the R package and manuscript, and a list of examplesof WORCS projects, are available at https://github.com/cjvanlissa/worcs.

scholarly journals BioInstaller: a comprehensive R package to integrate bioinformatics resources

2018 ◽  
Author(s):  
Jianfeng Li ◽  
Bowen Cui ◽  
Yuting Dai ◽  
Ling Bai ◽  
Jinyan Huang
Keyword(s):  
Source Code ◽  
R Package ◽  
Community Based ◽  
Representational State Transfer ◽  
State Transfer ◽  
Application Programming ◽  
Representational State ◽  
Programming Interfaces ◽  
Shiny Application ◽  
R Functions

The number of bioinformatics resources, such as tools/scripts and databases are growing exponentially. This poses a great challenge for users to access, manage, and integrate the corresponding bioinformatics resources. To facilitate the request, we proposed a comprehensive R package, BioInstaller, which includes the R functions, Shiny application, and the HTTP representational state transfer (REST) application programming interfaces (APIs). We also established a community-based configuration pool to collect, access and share bioinformatics resources. The source code of BioInstaller is freely available at our lab website http://bioinfo.rjh.com.cn/labs/jhuang/tools/bioinstaller or popular package host GitHub at: https://github.com/JhuangLab/BioInstaller. Also, a docker image can be downloaded from DockerHub (https://hub.docker.com/r/bioinstaller).

scholarly journals fullsibQTL: an R package for QTL mapping in biparental populations of outcrossing species

2020 ◽  
Author(s):  
Rodrigo Gazaffi ◽  
Rodrigo R. Amadeu ◽  
Marcelo Mollinari ◽  
João R. B. F. Rosa ◽  
Cristiane H. Taniguti ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Open Source ◽  
Qtl Analysis ◽  
Source Code ◽  
R Package ◽  
Genetic Maps ◽  
Linkage Phase ◽  
Position Effects ◽  
Genetic Features ◽  
Outcrossing Species

ABSTRACTAccurate QTL mapping in outcrossing species requires software programs which consider genetic features of these populations, such as markers with different segregation patterns and different level of information. Although the available mapping procedures to date allow inferring QTL position and effects, they are mostly not based on multilocus genetic maps. Having a QTL analysis based in such maps is crucial since they allow informative markers to propagate their information to less informative intervals of the map. We developed fullsibQTL, a novel and freely available R package to perform composite interval QTL mapping considering outcrossing populations and markers with different segregation patterns. It allows to estimate QTL position, effects, segregation patterns, and linkage phase with flanking markers. Additionally, several statistical and graphical tools are implemented, for straightforward analysis and interpretations. fullsibQTL is an R open source package with C and R source code (GPLv3). It is multiplatform and can be installed from https://github.com/augusto-garcia/fullsibQTL.

IPDfromKM: Reconstruct Individual Patient Data from Published Kaplan-Meier Survival Curves

2020 ◽  
Author(s):  
Na Liu ◽  
Yanhong Zhou ◽  
J. Jack Lee
Keyword(s):  
Survival Data ◽  
Individual Patient Data ◽  
Secondary Analysis ◽  
R Package ◽  
Patient Data ◽  
Survival Curves ◽  
Raw Data ◽  
Kaplan Meier ◽  
Number Of Patients ◽  
Shiny Application

Abstract BackgroundWhen applying secondary analysis on published survival data, it is critical to obtain each patient’s raw data, because the individual patient data (IPD) approach has been considered as the gold standard of data analysis. However, researchers often lack access to the IPD. We aim to propose a straightforward and robust approach to help researchers to obtain IPD from published survival curves with a friendly software platform. ResultsImproving upon the existing methods, we proposed an easy-to-use, two-stage approach to reconstruct IPD from published Kaplan-Meier (K-M) curves. Stage 1 extracts raw data coordinates and Stage 2 reconstructs IPD using the proposed method. To facilitate the use of the proposed method, we develop the R package IPDfromKM and an accompanied web-based Shiny application. Both the R package and Shiny application can be used to extract raw data coordinates from published K-M curves, reconstruct IPD from data coordinates extracted, visualize the reconstructed IPD, assess the accuracy of the reconstruction, and perform secondary analysis on the IPD. We illustrate the use of the R package and the Shiny application with K-M curves from published studies. Extensive simulations and real world data applications demonstrate that the proposed method has high accuracy and great reliability in estimating the number of events, number of patients at risk, survival probabilities, median survival times, as well as hazard ratios. ConclusionsIPDfromKM has great flexibility and accuracy to reconstruct IPD from published K-M curves with different shapes. We believe that the R package and the Shiny application will greatly facilitate the potential use of quality IPD data and advance the use of secondary data to make informed decision in medical research.

Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data

2017 ◽  
pp. 41-70 ◽  
Author(s):  
Arunkumar Chinnaswamy ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Information Gain ◽  
Feature Subset ◽  
Classification Problems ◽  
Raw Data ◽  
Correlation Based Feature Selection ◽  
Feature Selection Approach ◽  
Gene Expression Levels

The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.

scholarly journals aCLImatise: automated generation of tool definitions for bioinformatics workflows

2020 ◽  
Author(s):  
Michael Milton ◽  
Natalie Thorne
Keyword(s):  
Source Code ◽  
Supplementary Information ◽  
Command Line ◽  
Supplementary Data ◽  
Automated Generation ◽  
Base Camp ◽  
Python Package ◽  
Bioinformatics Workflow ◽  
Bioinformatics Workflows

Abstract Summary aCLImatise is a utility for automatically generating tool definitions compatible with bioinformatics workflow languages, by parsing command-line help output. aCLImatise also has an associated database called the aCLImatise Base Camp, which provides thousands of pre-computed tool definitions. Availability and implementation The latest aCLImatise source code is available within a GitHub organisation, under the GPL-3.0 license: https://github.com/aCLImatise. In particular, documentation for the aCLImatise Python package is available at https://aclimatise.github.io/CliHelpParser/, and the aCLImatise Base Camp is available at https://aclimatise.github.io/BaseCamp/. Supplementary information Supplementary data are available at Bioinformatics online.

Outliers in L2 Research in Applied Linguistics: A Synthesis and Data Re-Analysis

2020 ◽  
Vol 40 ◽  
pp. 26-55 ◽  
Author(s):  
Christopher Nicklin ◽  
Luke Plonsky
Keyword(s):  
Phase I ◽  
Relative Effectiveness ◽  
Reaction Time Data ◽  
Future Research ◽  
Time Data ◽  
Two Phase ◽  
Raw Data ◽  
Study Results ◽  
Using Data ◽  
The Impact

AbstractData from self-paced reading (SPR) tasks are routinely checked for statistical outliers (Marsden, Thompson, & Plonsky, 2018). Such data points can be handled in a variety of ways (e.g., trimming, data transformation), each of which may influence study results in a different manner. This two-phase study sought, first, to systematically review outlier handling techniques found in studies that involve SPR and, second, to re-analyze raw data from SPR tasks to understand the impact of those techniques. Toward these ends, in Phase I, a sample of 104 studies that employed SPR tasks was collected and coded for different outlier treatments. As found in Marsden et al. (2018), wide variability was observed across the sample in terms of selection of time and standard deviation (SD)-based boundaries for determining what constitutes a legitimate reading time (RT). In Phase II, the raw data from the SPR studies in Phase I were requested from the authors. Nineteen usable datasets were obtained and re-analyzed using data transformations, SD boundaries, trimming, and winsorizing, in order to test their relative effectiveness for normalizing SPR reaction time data. The results suggested that, in the vast majority of cases, logarithmic transformation circumvented the need for SD boundaries, which blindly eliminate or alter potentially legitimate data. The results also indicated that choice of SD boundary had little influence on the data and revealed no meaningful difference between trimming and winsorizing, implying that blindly removing data from SPR analyses might be unnecessary. Suggestions are provided for future research involving SPR data and the handling of outliers in second language (L2) research more generally.

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