We show that vasculature loss precedes muscle wasting in cancer cachexia. Years of work summed up in this tweetorial.

Also includes high quality muscle single cell data via GEO.

Senescent cells contribute to disease and are found in many tissues but are hard to analyze because markers have been derived in culture and do not account for cell-type differences. Here, we define new signatures based on millions of single cells (1/5) www.nature.com/articles/s41...

Not checking nuclear markers like MALAT1 or intronic reads in your scRNA-seq data?🚨
We show their power to flag low-quality cells—even in top public datasets. It’s time to prioritize better QC for cleaner, more reliable genomics research!
Read more: bmcgenomics.biomedcentral.com/articles/10....
1/8

Doublet detection methods simulate doublets by adding or averaging exp in random cell pairs. But, there is technical variation between cells. A true doublet is 2+ cells processed in the same droplet with no technical variation. Eg, cell A (9000 UMI)+ cell B (1000 UMI) still looks like cell A.

Typical single-cell preprocessing can be unfair to certain cell types. For example, fewer genes are typically detected in neutrophils and they are often mistakenly removed.

I’ve made a short video covering this simple but important concept:

youtu.be/r4A_QgseUfw?...

There are so many people moving over that I'm sure I'm missing folks. Can we make a #compbio / #genomics intro thread to get reacquainted?

I'm at the University of Colorado. I often say that if you pick two of three from #transcriptome, #ML, and #publicdata, my lab is probably interested.

Mark(@sanbomics.bsky.social) put together a really nice video & walkthrough on using alevin-fry/simpleAF to process your single-cell RNA-seq data. If you're doing processing of such data, I recommend checking it out as a transparent & open alternative to CellRanger (& way faster) t.co/Hu9ueRduQ9! 🖥️🧬