Single Cell Rna Qc

What it does

This skill performs quality control checks on single-cell RNA sequencing data to ensure dataset integrity before downstream analysis. It identifies and flags low-quality cells, such as those with high mitochondrial gene expression or excessive ambient RNA contamination.

When to use it

• Before running differential expression analysis to prevent skewed results from dead or dying cells.
• During initial data processing pipelines to filter out technical artifacts early in the workflow.
• When integrating multiple samples to standardize quality thresholds across different experimental batches.
• To validate raw sequencing output before investing time in complex clustering or trajectory inference.

Key capabilities

• Detects and removes low-quality cells based on mitochondrial gene percentage thresholds.
• Identifies potential doublets or multiplets within the cell population.
• Filters out ambient RNA contamination to improve gene expression accuracy.
• Generates quality metrics reports for manual review or automated gating.

Example prompts

• "Run quality control checks on my single-cell RNA dataset and remove cells with more than 20% mitochondrial reads."
• "Identify potential doublets in this scRNA-seq sample using standard contamination thresholds."
• "Filter out ambient RNA noise from my raw sequencing data before proceeding to clustering."

Tips & gotchas

Ensure your reference genome annotation is up-to-date, as outdated gene symbols can lead to incorrect mitochondrial percentage calculations. Always review the generated quality metrics visually, as automated filters may occasionally remove biologically relevant rare cell types if thresholds are set too aggressively.