Object-oriented programming is a common paradigm in programming. In my actual work, using object-oriented programming mainly serves two purposes: reducing duplicate code through inheritance features, and encapsulating frequently used data into objects to avoid excessive, repetitive, and nested parameter passing.

After getting used to Python, developing scripts in R can feel quite painful. Putting aside the often-discussed issues with unclear error tracing, when scripts become slightly more complex and need to be split into multiple files, I realized R’s import mechanism is also quite frustrating… Fortunately, there’s the box package that allows module imports similar to Python’s logic.

Compile the OpenFyde Image for Fydetab Duo by synthesizing official instructions and forum articles, aiming to compile the latest R120 version image. The goal is to compile the latest R120 version image.

I’ve had the Fydetab Duo for over a month, and I’m still trying to find ways to make the most use of this device. At the very least, I’d like to write some blogs during my downtime with it. Hence the following exploration.

Looking at the creation time of this draft, it was actually June 2024… Now it’s June 2025, and I suddenly understand why so many content creators become “pigeons” (procrastinators). Starting projects is fun, but finishing them is painful… This is one of my few pure bioinformatics posts…

The problem originated last year when I needed to run monocle3 for pseudotime analysis, but encountered an annoying issue at the final stage. In monocle3, the starting point for pseudotime trajectory needs to be manually specified by the analyst. During R code execution, it automatically opens a browser where users need to specify the starting point on a temporary webpage, then close the page for the analysis to continue.

However, Jupyter’s irkernel doesn’t support this feature. This means I couldn’t complete the analysis directly in Jupyter notebook. This issue was first reported in 2019, but even by 2024 when I needed to do the analysis - five years later - there was still no solution…

Bioinformatics is an interdisciplinary field, and the toolkit or technology stack used in bioinformatics is also quite “interdisciplinary”. The level of fragmentation is, in my opinion, absolutely not less than that of Linux distributions… This also brings us a common challenge: the deployment of bioinformatics analysis environments.

Last year, I suddenly felt unhappy with the old laptop lying on my bed. It was a relatively expensive purchase for me (a big expense six months after graduation), and it had keyboard and touchpad issues that required two repairs costing another 1000 yuan each. Given its high cost, I didn’t want to leave it idle, so I decided to give fydeos another chance. After some research, I found that both deepin and fydeos have evolved significantly in recent years. Fydeos has become more user-friendly, and they now offer Android as well as a Linux subsystem, making daily use much more accessible. Therefore, I decided to buy a Lenovo Chromebook Duet.

Actually, I’ve known about singularity for quite some time. As a container specifically designed for HPC (High Performance Computing), it’s always been something I wanted to try out. However, just like other NGS technologies outside of Illumina, singularity hasn’t gained much traction and it seems even harder to compete with Kubernetes, which is widely adopted by most cloud vendors… Of course, this doesn’t really matter to me right now… We’re still at least three or five years away from going cloud-based, so using it locally makes perfect sense.