Showing 4 of total 4 results (show query)
bioc
PCAtools:PCAtools: Everything Principal Components Analysis
Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.
Maintained by Kevin Blighe. Last updated 5 months ago.
rnaseqatacseqgeneexpressiontranscriptionsinglecellprincipalcomponentcpp
348 stars 11.12 score 832 scripts 2 dependents
diondetterer
epinetr:Epistatic Network Modelling with Forward-Time Simulation
Allows for forward-in-time simulation of epistatic networks with associated phenotypic output.
Maintained by Dion Detterer. Last updated 3 years ago.
3.70 score 9 scripts
gk-crop
simplaceUtil:Provides Utility Functions and ShinyApps to work with the modeling framework 'SIMPLACE'
Provides Utility Functions and ShinyApps to work with the modeling framework 'SIMPLACE'. It visualises components of a solution, runs simulations and displays results.
Maintained by Gunther Krauss. Last updated 1 months ago.
1 stars 3.48 score 2 scripts
voiceanalyticshub
voiceR:Voice Analytics for Social Scientists
Simplifies and largely automates practical voice analytics for social science research. This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 <doi:10.1016/j.jbusres.2020.09.020>.
Maintained by Francesc Busquet. Last updated 2 years ago.
1.00 score 6 scripts