Milo is a differential abundance analysis method for single-cell RNA sequencing data that can detect compositional changes in cell neighborhoods across different conditions.

In cancer research, comparing genomic features between tumor samples and organoid models is crucial for validating model reliability. Circos plots provide an intuitive way to visualize detected mutations across the genome, making them commonly used for representing overall detection results of representative samples. While reading the circlize documentation, I came across an example demonstrating paired samples, which I found suitable for showcasing paired primary samples and organoids. I’ve adapted it to create a plot for displaying paired samples. The code is primarily based on the official documentation’s 9.5 Concatenating two genomes

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.

mSINGS is a software tool used to detect MSI (Microsatellite Instability). Its advantage seems to be that it can be used for tumor-only samples.

Damn, I finally successfully used this feature… I’m really stupid…

MultiQC is a tool for NGS data quality control. Unlike many other tools, it does not directly obtain and calculate metrics; instead, it reads results files from various common quality control tools and provides comprehensive reporting.

Lumpy’s author positioned this project more as building a general structural variant signal generation scheme rather than creating an excellent tool. It was implemented as a basic version of the scheme. Compared to other tools, Lumpy spent more time in its article explaining the evidence concepts and methods for obtaining information. Interpreting this tool has made me understand better how to obtain structural variant signals from NGS sequencing data.

“I know a lot about structural variation information,” but the content of this blog post is too much, and it’s not convenient for me to look back. So I decided to split it into smaller parts and write a comprehensive one using GitBook later.

Writing workflows is a common task in bioinformatics analysis, and a mature and well-designed tool can greatly improve the efficiency of work.