Displaying 1 to 8 from 8 results

biomartr - Genomic Data Retrieval with R

  •    R

This package is born out of my own frustration to automate the genomic data retrieval process to create computationally reproducible scripts for large-scale genomics studies. Since I couldn't find easy-to-use and fully reproducible software libraries that would allow others and me to write transparent and easy to reproduce code, I sat down and tried to implement a framework that would enable anyone to automate the genomic data retrieval process. Personally, I strongly support and believe in reproducible research, and I truly hope that this package might be useful to others as well and that it helps to promote reproducible research in genomics studies.I happily welcome anyone who wishes to contribute to this project :) Just drop me an email.

rvtests - Rare variant test software for next generation sequencing data

  •    C++

Rvtests, which stands for Rare Variant tests, is a flexible software package for genetic association analysis for sequence datasets. Since its inception, rvtests was developed as a comprehensive tool to support genetic association analysis and meta-analysis. It can analyze both unrelated individual and related (family-based) individuals for both quantitative and binary outcomes. It includes a variety of association tests (e.g. single variant score test, burden test, variable threshold test, SKAT test, fast linear mixed model score test). It takes VCF/BGEN/PLINK format as genotype input file and takes PLINK format phenotype file and covariate file. With new implementation of the BOLT-LMM/MINQUE algorithm as well as a series of software engineering optimizations, our software package is capable of analyzing datasets of up to 1,000,000 individuals in linear mixed models on a computer workstation, which makes our tool one of the very few options for analyzing large biobank scale datasets, such as UK Biobank. RVTESTS supports both single variant and gene-level tests. It also allows for highly effcient generation of covariance matrices between score statistics in RAREMETAL format, which can be used to support the next wave of meta-analysis that incorporates large biobank datasets.

esc - Effect Size Computation for Meta Analysis

  •    R

This is an R implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson. The original calculator can be found at http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php. Based on the input, the effect size can be returned as standardized mean difference (d), Cohen's f, eta squared, Hedges' g, correlation coefficient effect size r or Fisher's transformation z, odds ratio or log odds effect size.

CanlabCore - Core tools required for running Canlab Matlab toolboxes

  •    Matlab

This repository contains core tools for MRI/fMRI/PET analysis from the Cognitive and Affective Neuorscience Lab (Tor Wager, PI) and our collaborators. Many of these functions are needed to run other toolboxes, e.g., the CAN lab’s multilevel mediation and Martin Lindquist’s hemodynamic response estimation toolboxes. A brief introduction to the toolbox can be found here. The tools include object-oriented tools for doing neuroimaging analysis with simple commands and scripts that provide high-level functionality for neuroimaging analysis. For example, there is an "fmri_data" object type that contains neuroimaging datasets (both PET and fMRI data are ok, despite the name). If you have created and object called my_fmri_data_obj, then plot(my_fmri_data_obj) will generate a series of plots specific to neuroimaging data, including an interactive brain viewer (courtesy of SPM software). predict(my_fmri_data_obj) will perform cross-validated multivariate prediction of outcomes based on brain data. ica(my_fmri_data_obj) will perform independent components analysis on the data, and so forth.

metafor - A Meta-Analysis Package for R

  •    R

The metafor package is a comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbé, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1-48. http://www.jstatsoft.org/v36/i03/.

JamoviMeta - Meta-Analysis for JAMOVI

  •    R

I have rebranded this project as MAJOR: Meta Analysis JamOvi R which can be found here https://github.com/kylehamilton/MAJOR Everything is the same as before just some minor polish to the UI and a rebranding of the name.

MAVIS - MAVIS: Meta Analysis via Shiny

  •    R

W. Kyle Hamilton would like to thank the Health Communications and Interventions Lab at UC Merced for their comments and beta testing efforts on this application as well as Kathleen Coburn for her feedback and evaluation of the statistical methods related to this project. Atsushi Mizumoto would like to thank Dr. Luke Plonsky and Dr. Yo In'nami for their support and feedback to create this web application.