If you updated Seurat recently, you might find your FeaturePlot() and DimPlot() giving plots that look pixelated instead of the circular dots we are used to.
Don’t panic, this is because in a recent update, raster = TRUE becomes default for FeaturePlot() and DimPlot() when your dataset contains more than 50,000 cells.
The problem When compiling Emacs 28.05, make bootstrap failed with fatal error: 'libxml/tree.h' file not found while autogen.sh and configure were successfully executed.
Troubleshooting The error message pointed out the culprit, libxml.
I am exaggerating, but sometimes stringsAsFactors is almost this deadly. I work with genomic data, and a common quest in my job is to identify interesting features (in most cases, genes) from a pool of 25,000+.
After struggling for a while, I decided to move from Medium and switch to blogdown. While Medium is a beautiful platform for blogging, its philosophy seems to fit less well when there are more than articles to host.
The more I work with Linux, the more I encounter dependency issues. This is of course not too big a surprise, but it can be painful especially when you aren’t sudo, so the most obvious solution does not work for you.
Recently, I am continuously being amazed by how a seemingly simple task is actually implemented in a sophisticated way. I guess I am just taking so many things for granted just because it was implemented and refined to an extent that I don’t even feel it.
I updated my R packages the other day, and not surprisingly, one package failed to compile. This time, it was XML. The error message suggested configure: error: “libxml not found”, but homebrew suggested I had installed libxml2 and had it up-to-date.
Oftentimes, the sample I deal with is full of noise or confounding factors that I am not interested in. For example, human specimen is doomed noisy because the race, age, sex, occupation, or the life story of the subject would have influenced the results.
As a novice in genomic data analysis, one of my goal is to benchmark how well a clustering method works. I ran across this practice of doing k-means at R-exercises the other day and felt it might be a nice start because k-means is easy to perform and conceptually simple for me to correlate what is happening behind the clustering machinery.