An adjacency list is simply an unordered list that describes connections between vertices. It's a commonly used input format for graphs. In this post, I use the melt() function from the reshape2 package to create an adjacency list from a correlation matrix. I use the geneData dataset, which consists of real but anonymised microarray expression data, from the Biobase package as an example. Finally, I'll show some features of the igraph package.
A colleague asked me this question: "I'm trying to find a way to find genes that overlap between three datasets. I have used intersect for two dataframes but can't seem to find a solution for three dataframes on google. Do you know any snazzy way of doing that?" I thought using the venn() function from the gplots package is a pretty snazzy solution, so that's what I recommended.
I created a basic Shiny app that uses the myvariant package to fetch variant information from MyVariant.info. The variants need to be represented in the format recommended by the Human Genome Variation Society. Once you have your variant of interest in the correct format, just hit "Get variant info!" and the annotations will appear on the right. You can find the app hosted at: https://davetang.shinyapps.io/get_variant_info/.
A post on creating a Gantt chart using R; these are good for showing a project timeline in grant applications. The code is adapted from an answer on Stack Overflow. You will need the ggplot2 and reshape2 packages; install them if you haven't already.
A quick and short post on parallel distance calculation in R using the mclapply() function from the parallel package. I'll use data from the Biobase and datamicroarray packages to illustrate.
I recently completed Data Manipulation in R with dplyr and realised that dplyr can be used to aggregate and summarise data the same way that aggregate() does. I wrote a post on using the aggregate() function in R back in 2013 and in this post I'll contrast between dplyr and aggregate().