RIPSERR: AN R PACKAGE FOR EFFICIENT COMPUTATION OF PERSISTENT HOMOLOGY

Calculating persistent homology of a simplicial complex is a critical step in most topological data analysis pipelines. The C++ Ripser library calculates persistent homology more rapidly than other contemporary software packages. However, given that R is more commonly used for data analysis, incorporating C++ code into an R pipeline can create unintended inefficiencies that nullify any benefits gained by switching to Ripser. The ripserr R package uses Rcpp to efficiently incorporate a modified Ripser into an R pipeline by encapsulating it within an R function. This not only preserves Ripser's efficiency, but also allows it to be as easily used as any other R function. The ripserr package can be found in our GitHub repository. See the README file in the GitHub repo for details on installation and usage.

SIGQC - A NEW STANDARD FOR GENE SIGNATURE QUALITY CONTROL

sigQC is an R package, available on CRAN, that we have worked in collaboration to develop, defining an integrated methodology for gene signature quality control. Increasing amounts of genomic data mean that gene expression signatures are becoming critically important tools, poised to make a large impact on the diagnosis, management and prognosis for a number of diseases. For the purposes of this package, we define the term gene signature to mean: ‘a set of genes whose co-ordinated mRNA expression pattern is representative of a biological pathway, process, or phenotype, or clinical outcome.’
A key issue with gene signatures of this nature is whether the expression of many genes can be summarised as a single score, or whether multiple components are represented. In this package, we have automated the testing of a number of quality control metrics designed to test whether a single score, such as the median or mean, is an appropriate summary for the genes’ expression in a dataset. The tools in this package enable the visualization of properties of a set of genes in a specific dataset, such as expression profile, variability, correlation, and comparison of methods of standardisation and scoring metrics.

Read more about it on Andrew’s blog, or on the preprint.
Try it for yourself today as well -- CRAN package.


egtplot: A Python Package for 3-Strategy Evolutionary Games

Evolutionary game theory is a very broad modeling framework that effectively describes many aspects of biological cooperation and competition. Visualization of three-strategy evolutionary games has historically been difficult within the Python ecosystem. We have created a package to ease visualization efforts that is capable of displaying both static and animated dynamics with the game space. For detailed software usage instructions we refer to our interactive jupyter notebook. We also welcome comments and questions regarding our whitepaper on bioRxiv.
See our GitHub repository for detailed installation instructions.