![]() ![]() ![]() ![]() In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. ![]() These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license ( ). The development of independent layers of interactivity has been in pursuit in the visualization community. Interactive data visualization is imperative in the biological sciences. As linked in the Bioconductor website, the bigPint package also has a vignette website ( ), where users can follow reproducible code to install the software and use the example datasets to create bigPint graphics and follow an example analysis pipeline. The package can be downloaded from the Bioconductor website ( ). The bigPint package itself comes with two of the aforementioned RNA-sequencing datasets as examples. The original dataset we used in our recent methods paper is also available on the NCBI Gene Expression Omnibus with accession number GSE121885. One is deposited on the NCBI Gene Expression Omnibus with accession number GSE61857. The four public datasets used in our recent methods paper are available online: Three are deposited on the NCBI Sequence Read Archive with accession numbers SRA000299, PRJNA318409, and SRA048710. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |