I recently tested a small bioinformatics data analysis workflow.
This skill was not written by me. I downloaded a biomedical research/data analysis skill from an open skill library and tried to apply it to the analysis results of single-cell RNA-seq.
This case is the scRNA-seq analysis of tumor tissue vs adjacent normal tissue. The actual analysis has already been run outside of OpenClaw using a standard single-cell pipeline.
What I provided to OpenClaw was not the raw data, but the already organized analysis outputs and notes:
Cell type annotation results
Several marker genes of the clusters
Change in the proportion of immune cells
Macrophage subpopulation markers
Differential expression results
Pathway enrichment results
A brief description of the sample grouping and analysis setup
I use it to review and analyze the results, not to have it generate the results itself.
The useful aspect of it is that it can help me ask better questions based on the results, such as whether it is supported by known markers? Is it just a general inflammatory pathway?
The most useful restriction I added is:
Do not write the final biological conclusion. Please review the analysis results, mark the weak explanations, and list the areas that need verification.
After adding this sentence, the output is significantly better.
It no longer tries to write a beautiful conclusion in the style of a paper, but more like a secondary reviewer of the analysis results.
For me, the value of OpenClaw in bioinformatics is not to replace Seurat, Scanpy, or real analysts, but to help check the chaotic analysis outputs and prevent them from becoming an overly confident biological story too quickly.