i can help myself with a blog or something

I have gone deep into the world of agentic AI and have been applying it to my genomics work. Academic research lab scRNAseq, spatial omics, epigenomics etc I have used all kinds of agent harnesses, skills, connectors and extensions. Everyone seems convinced it is the future of our field and if we don’t get ahead of it we will be left behind. But I feel like my progress is much slower now, much messier I spend a lot of time doing directory archaeology. I understand less of what I produce. I thoroughly don’t enjoy my work anymore. Should I risk getting left behind and give up with AI and agentic bioinformatics, for the sake of my sanity?

I want to investigate whether a transcriptionally distinct intermediate state exists between two previously characterized cell populations.

The relevant scRNA-seq dataset only has raw SRA reads available..no processed matrix or Seurat object. Reconstructing it would require ~150 GB of FASTQs and a custom reference, which could take significant time and compute.

My main concern is that there is only one library per condition, so the reconstructed data wouldn't provide biological replication for strong condition-level statistical comparisons.

However, cell-level data may be necessary for my specific question, since the available cluster summaries aren't sufficient.

Would reconstructing the dataset be worthwhile, or is there a sensible way to scope the analysis down while still answering the biological question?

Does it make biological sense to interpret peptide length as a potential source of systematic difficulty in ACE-inhibition prediction (different presentation / models (regression) ) or should I avoid making that interpretation and frame this purely as a data/prediction phenomenon??? and what if i cross veify those peptides on different publicly available datasets ?

Hi all,

I’ve been working with a Xenium 500-plex spatial dataset for the past year and, although I’ve been able to make good use of it overall, there are still a few aspects of the analysis that I haven’t fully resolved.

Annotation:
My cell-type annotation is based mainly on manual annotation of clusters from the integrated dataset, as I have not been able to fully annotate the tissue using Tangram because the available gold-standard scRNA-seq references do not capture all of the cell populations present in my samples.

The clusters are generated using graph-based Leiden clustering on Harmony dimensions derived from SCTransform-normalised data. One issue I consistently encounter is that, within most annotated clusters, a proportion of cells falls away from the main cluster centroid on the UMAP. I suspect this may partly reflect spatial transcript bleeding or contamination from neighbouring cells, although spatial bleeding correction itself require good enough references and/or alters counts making use of normalisation like SCTransform not feasible (and my samples have variable quality). My tissue also does not have a well-defined organisation of cell types into discrete anatomical structures, so approaches such as SpaNorm do not seem particularly well suited to the dataset.

DEG analysis of co-localised cells:
A related issue arises when performing differential expression analysis on spatially co-localised cells. For example, when comparing a cell type of interest X depending on whether it is located close to cell type Y, I find it difficult to determine whether increased expression of certain genes in X reflects a genuine transcriptional response of X to its proximity to Y, or simply transcripts originating from Y being incorrectly assigned to neighbouring X cells.

I’d be very interested to hear how others working with Xenium data have approached either of these issues.