UpSetR - A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets
Creates visualizations of intersecting sets using a novel matrix design, along with visualizations of several common set, element and attribute related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.
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ggplot2hidivelabupsetupsetrvisualization
15.56 score 829 stars 40 dependents 5.8k scripts 32k downloadsscde - Single Cell Differential Expression
The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
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immunooncologyrnaseqstatisticalmethoddifferentialexpressionbayesiantranscriptionsoftwareanalysisbioinformaticsheterogenityngssingle-celltranscriptomicsopenblascppopenmp
7.64 score 180 stars 174 scripts 858 downloadsEHRtemporalVariability - Delineating Temporal Dataset Shifts in Electronic Health Records
Functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. 'EHRtemporalVariability' is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. 'EHRtemporalVariability' allows you to explore and identify dataset shifts through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional 'EHRtemporalVariability' Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding. (Sáez et al. 2020) <doi:10.1093/gigascience/giaa079>.
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biomedical-data-sciencebiomedical-informaticsdata-qualitydata-quality-monitoringdataset-shiftselectronic-health-recordstimevariabilityvisualization
4.93 score 17 stars 455 downloads