Medical Systems Biology

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colorpatch

colorpatch is an R-package for optimized rendering of fold changes and confidence values primarily designed for visualising gene expression profiles.

  • bivariate color maps (HSV color space),
  • a patch grid approach combined with a psychophysically optimized color scale.
Functionality for optimizing two-sided color scales is included.

The most recent version of the package and its documentation is available from colorpatch's CRAN page.

Documentation

Documentation is included in the R package. You can get help for a command by typing ?<command> (e.g. ?PlotUniformity) inside R when the package is loaded.

The vignette shows an example of this package. Call

vignette("introduction", package = "colorpatch")

within R after installing the package.

Installation

In an R console, type install.packages("colorpatch"). The package is now installed and can be loaded via library(colorpatch).

Latest News

 

Congratulations to Dr. Silke Werle for winning the 1st Prize with her pitch at the 1. Science Day held by ProTrainU. 

 

Our paper "Reconstructing Boolean network ensembles from single-cell data for unraveling dynamics in the aging of human hematopoietic stem cells" has been published in the Computational and Structural Biotechnology Journal.

 

The position paper "Is there a role for statistics in artificial intelligence" has been published online first in Advances in Data Analysis and Classification.

 

Our paper "Corona Health - A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" has been published in the International Journal of Environmental Research and Public Health.

 

Our paper "Patient empowerment during the COVID-19 pandemic: Ensuring safe and fast communication of test results" has been published in the Journal of Medical Internet Research.

 

Our paper "Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment" has been published in IEEE Access.

  

Our report protocol "Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol" has been published in PLoS One.

 

We are happy we could contribute to Beutel et al (2021) "A prospective Feasibility Trial to Challenge Patient-Derived Pancreatic Cancer Organoids in Predicting Treatment Response" published in MDPI Cancers.