- RMARKDOWN PLOT SIZE UPDATE
- RMARKDOWN PLOT SIZE PORTABLE
- RMARKDOWN PLOT SIZE SOFTWARE
- RMARKDOWN PLOT SIZE CODE
Previous versions instantiated plots within a new HTML window when Glimma R functions were called, creating a separation between interactive plots and the code that created them.
RMARKDOWN PLOT SIZE SOFTWARE
This would make it easier for bioinformaticians and software engineers to rapidly develop new features in response to new developments in gene expression studies, such as single cell RNA-seq (scRNA-seq) analysis.Īnother important theme in the second iteration of Glimma was reproducibility. Version 2.0 of Glimma was built to address these deficiencies by reproducing all existing functionality in 1.0 using existing high-level libraries such as htmlwidgets ( 15) and Vega ( 16). For instance, simple improvements such as adding plot legends and scaling point sizes became intractable tasks. The low-level nature of d3.js and the high complexity of the codebase made it difficult to add improved interactivity and new output formats to the package. Intended as a drop-in interactive visualization tool for common RNA-seq workflows, Glimma was built on d3.js ( 14) and relied on custom-built functions for connecting R code with a web-based frontend.
The software is commonly used for exploration of transcriptional data from raw gene expression-levels to summarized results obtained from DE analysis ( 13).
The responsive and user-friendly layout of Glimma 1.0 has proven very popular among the Bioconductor community, amassing over 19 000 downloads in 2020 alone. These could be exported as HTML files and shared with collaborators, allowing biologists to investigate interesting features in the data with minimal coding required. Glimma version 1 allowed the creation of two interactive versions of limma-style plots: a multidimensional scaling (MDS) plot used to assess variability between samples and a mean-difference (MD) plot used for identifying differentially expressed genes between experimental conditions. This allows the package to cater to its main user base of biologists and end-users who would like drop-in graphics for popular gene expression workflows.
RMARKDOWN PLOT SIZE PORTABLE
The Glimma software does not depend on Shiny and can produce portable outputs that can be viewed without an active installation of R. While many powerful tools exist for producing interactive plots for DE analysis ( 7–11), they require users to run a Shiny server ( 12), which may be difficult to navigate for those with minimal experience in R. Static graphics necessarily provide a flattened perspective on the data version 1 of Glimma ( 3) aimed to remedy this by allowing users to interactively explore data at the sample-level through dimensionality reduction plots and at the gene-level in plots of summary statistics obtained from popular DE analysis tools limma ( 4), edgeR ( 5) and DESeq2 ( 6). As there are tens of thousands of genes involved in DE analyses, it can be difficult to pinpoint information on genes of interest in densely populated static R ( 2) plots. Researchers commonly leverage RNA-seq technology to compare the transcription levels of genes across experimental conditions, in a workflow known as differential expression (DE) analysis. RNA-sequencing (RNA-seq) is a high-throughput method for characterizing transcriptomes ( 1). Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics.
RMARKDOWN PLOT SIZE UPDATE
Here, we present a major update to Glimma that brings improved interactivity and reproducibility using high-level visualization frameworks for R and JavaScript.
Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis.
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