4 ms·
There are so many arbitrary choices made when analyzing single cell RNA-seq data. There coverage cutoffs to decide when a gene is expressed, arbitrary QA point
by gww 10y ago
There are so many arbitrary choices made when analyzing single cell RNA-seq data. There coverage cutoffs to decide when a gene is expressed, arbitrary QA points to decide when a cell is "good quality", the PC's chosen for t-SNE, the genes identified as more variable than estimated levels of technical noise, etc etc. is very frustrating. This leads to huge issues with reproducibility, almost every single paper uses their own in house custom analysis pipelines and they rarely make them open source.
- lasso1 10y agoYes i know the feeling. Our field has some very arbitrary threshold for noise on single features, sounds like there is some slightly more principled strategy in single cell genomics?
- daemonk 10y agoThe main reason is because no one wants to publish technical or "methods" type papers where they assess the technology. There is usually one or two initial big papers introducing the technology that makes a big splash. No one subsequently will want to assess it or improve much on it because it won't publish well and you likely will not get cited for it anyways.