Showing 5 of total 5 results (show query)
gaospecial
ggVennDiagram:A 'ggplot2' Implement of Venn Diagram
Easy-to-use functions to generate 2-7 sets Venn or upset plot in publication quality. 'ggVennDiagram' plot Venn or upset using well-defined geometry dataset and 'ggplot2'. The shapes of 2-4 sets Venn use circles and ellipses, while the shapes of 4-7 sets Venn use irregular polygons (4 has both forms), which are developed and imported from another package 'venn', authored by Adrian Dusa. We provided internal functions to integrate shape data with user provided sets data, and calculated the geometry of every regions/intersections of them, then separately plot Venn in four components, set edges/labels, and region edges/labels. From version 1.0, it is possible to customize these components as you demand in ordinary 'ggplot2' grammar. From version 1.4.4, it supports unlimited number of sets, as it can draw a plain upset plot automatically when number of sets is more than 7.
Maintained by Chun-Hui Gao. Last updated 5 months ago.
set-operationsupsetupsetplotvenn-diagramvenn-plot
292 stars 12.31 score 1.3k scripts 4 dependents
bioc
iCOBRA:Comparison and Visualization of Ranking and Assignment Methods
This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results.
Maintained by Charlotte Soneson. Last updated 3 months ago.
14 stars 8.86 score 192 scripts 1 dependents
dzhakparov
GeneSelectR:Comprehensive Feature Selection Worfkflow for Bulk RNAseq Datasets
GeneSelectR is a versatile R package designed for efficient RNA sequencing data analysis. Its key innovation lies in the seamless integration of the Python sklearn machine learning framework with R-based bioinformatics tools. This integration enables GeneSelectR to perform robust ML-driven feature selection while simultaneously leveraging the power of Gene Ontology (GO) enrichment and semantic similarity analyses. By combining these diverse methodologies, GeneSelectR offers a comprehensive workflow that optimizes both the computational aspects of ML and the biological insights afforded by advanced bioinformatics analyses. Ideal for researchers in bioinformatics, GeneSelectR stands out as a unique tool for analyzing complex RNAseq datasets with enhanced precision and relevance.
Maintained by Damir Zhakparov. Last updated 10 months ago.
19 stars 4.98 score 7 scripts
core-bioinformatics
bulkAnalyseR:Interactive Shiny App for Bulk Sequencing Data
Given an expression matrix from a bulk sequencing experiment, pre-processes it and creates a shiny app for interactive data analysis and visualisation. The app contains quality checks, differential expression analysis, volcano and cross plots, enrichment analysis and gene regulatory network inference, and can be customised to contain more panels by the user.
Maintained by Ilias Moutsopoulos. Last updated 1 years ago.
27 stars 4.47 score 11 scriptsbioc
PRONE:The PROteomics Normalization Evaluator
High-throughput omics data are often affected by systematic biases introduced throughout all the steps of a clinical study, from sample collection to quantification. Normalization methods aim to adjust for these biases to make the actual biological signal more prominent. However, selecting an appropriate normalization method is challenging due to the wide range of available approaches. Therefore, a comparative evaluation of unnormalized and normalized data is essential in identifying an appropriate normalization strategy for a specific data set. This R package provides different functions for preprocessing, normalizing, and evaluating different normalization approaches. Furthermore, normalization methods can be evaluated on downstream steps, such as differential expression analysis and statistical enrichment analysis. Spike-in data sets with known ground truth and real-world data sets of biological experiments acquired by either tandem mass tag (TMT) or label-free quantification (LFQ) can be analyzed.
Maintained by Lis Arend. Last updated 12 days ago.
proteomicspreprocessingnormalizationdifferentialexpressionvisualizationdata-analysisevaluation
2 stars 4.41 score 9 scripts