What are the key bioinformatic QC metrics for SeekOne DD 3' scRNA-seq data?

Answer:
QC for 3´ scRNA-seq is divided into three levels: sequencing quality (library structure, read accuracy), mapping performance (alignment to genome and annotation), and cell-level statistics (estimated cell number, reads per cell, genes per cell). These metrics help determine whether the library is suitable for downstream analysis such as clustering and differential expression.

Because QC metrics are strongly influenced by sample type, dissociation quality, nuclei integrity, sequencing depth, and reference annotation quality, metrics should always be interpreted collectively rather than in isolation.

For nuclei-derived samples (snRNA-seq), intronic RNA signals are expected to be substantially enriched relative to conventional whole-cell scRNA-seq datasets.

Sequencing QC Metrics

RNA Mapping QC Metrics

Overall Genome Mapping Metrics

Note: H/M indicates Human/Mouse

Transcript Distribution Metrics — snRNA-seq

For nucleus-derived RNA sequencing data (snRNA-seq), intronic reads are expected to be substantially enriched because a large proportion of nuclear RNA consists of unspliced pre-mRNA molecules.

Transcript Distribution Metrics — scRNA-seq

For conventional whole-cell scRNA-seq datasets, mature cytoplasmic mRNA is typically enriched, resulting in higher exonic mapping rates and lower intronic proportions compared with snRNA-seq.

Cell-Level QC Metrics