#shinyly

[balanceflow]

Neu Shinyly News im shinyly.shop/ - schau doch einfach mal rein 🌱 🐝 🌸

[kidfoundonstreets]

sevelral shiny clean years ago i shinyly cleanly wirite a shiny clean story about shiny clean character named smarts intellct who everyone lvoes and cherishes and one shiny clean day is swoop into shiny clean battle. i rembmer i created a so shiny clean magic sysytem i thoguth it was very shiny clean. also said shiny clean with everything. then i dellted it years later

shsiny clean delleted foroever. eveyrday shiny clena wonder why delleted. shiny cleran intelelct an eveyrbody cheirsh dont rmemebr anymore. restin peace shiny clea shiny clean so shiny. imlvoe shiny clean. untitl shiny clelan. shiny clelan imkill

[cancersfakianakis1]

GRcalculator: an online tool for calculating and mining dose–response data

Abstract

Background

Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose–response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521–627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose–response assays and the reliability of pharmacogenomic associations (Hafner et al. 500–502, 2017).

Results

We describe here an interactive website (www.grcalculator.org) for calculation, analysis, and visualization of dose–response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at http://ift.tt/2i1KjHL and http://ift.tt/2gA4zQn.

Conclusions

GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose–response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose–response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program (http://ift.tt/1LdKiq5/) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose–response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.

http://ift.tt/2gyybxv

[balanceflow]

shinyly - win balance ist heute 6 geworden!

[balanceflow]