On the sample-wise genes-versus-UMI plot already suggested, color the cells by the percentage of raw counts in their 50 most expressed genes. [Scanpy documents this metric](https://scanpy.readthedocs.io/en/stable/generated/scanpy.pp.calculate_qc_metrics.html) as a library-complexity measure. Higher percentages at comparable UMI depth would indicate counts concentrated in fewer genes, although that alone wouldn't establish poor quality. What is the median top-50 percentage for each suspect cluster versus other cells at similar depth within each sample?
Add `pct_counts_in_top_50_genes` and compare the suspect cluster within each sample. A high fraction indicates that a few genes dominate the library, which supports low complexity even when total UMIs are high. Current Scanpy documentation defines this metric specifically for assessing library complexity. What is the cluster’s median `pct_counts_in_top_50_genes` in each sample?
Add the fraction of counts in the top 20 or 50 genes to the UMI versus detected-gene plot. If the suspect cluster has a sharply elevated top-gene fraction within each sample, its high UMI count may reflect low transcriptomic complexity rather than a rare state. What is the cluster’s median `pct_counts_in_top_50_genes` relative to neighboring cells?
