Introduction In a recent blog post we introduced 7 Interactive Bioinformatics Plots Made in Python and R. Here I introduced 7 Interactive Plots from the Pharmaceutical Industry using the plotly R package. These plots are essential for any survival analysis study, where there is interest in time-to-events as often seen in the ... [Read more...]

Introduction In a recent blog post, I introduced the new R package, manhattanly, which creates interactive manhattan and Q-Q plots using the plotly.js engine. In the latest CRAN release, you can also create volcano plots. In this post, I describe how to create interactive volcano plots using the manhattanly ... [Read more...]

The Problem When clustering data using principal component analysis, it is often of interest to visually inspect how well the data points separate in 2-D space based on principal component scores. While this is fairly straightforward to visualize with a scatterplot, the plot can become cluttered quickly with annotations as ... [Read more...]

Introduction In a recent blog post, I introduced the new R package, manhattanly, which creates interactive manhattan plots using the plotly.js engine. In this post, I describe how to create interactive Q-Q plots using the manhattanly package. Q-Q plots tell us about the distributional assumptions of the observed test ... [Read more...]

Introduction The new R package, manhattanly, creates interactive manhattan plots using the plotly.js engine. The plots are usable from the R console, the RStudio viewer pane, R Markdown documents, in Shiny apps, embeddable in websites and can be exported as .png files. By hovering the mouse over a point, ... [Read more...]

When performing Student’s t-test to compare the difference in means between two groups, it is a useful exercise to determine the effect of unequal sample sizes in the comparison groups on power. Formally, power can be defined as the probability of rejecting the null hypothesis when the alternative hypothesis ... [Read more...]

In every statistical analysis, the first thing one should do is try and visualise the data before any modeling. In microarray studies, a common visualisation is a heatmap of gene expression data. In this post I simulate some gene expression data and visualise it using the heatmaply package in R ... [Read more...]

Git and GitHub are indispensable tools for anyone analysing data, developing software or disseminating results. Originally designed for software engineers, GitHub is now widely used in many disciplines, especially for researchers in academia. Having a source code management software such as GitHub to host your code and have detailed project ... [Read more...]