bulkrna-alignment

Quality control for a bulk RNA-seq count matrix involves assessing library size distribution, gene detection rates, and sample-to-sample correlation to identify outlier samples. This Skill generates visual reports and structured data using pandas and matplotlib to ensure data integrity.

Detecting outlier samples in RNA-seq data requires evaluating sample-to-sample correlation and library size distribution. By generating visual reports of these metrics, you can identify problematic samples and ensure count matrix reliability before proceeding with downstream differential expression analysis.

Checking library size distribution and gene detection rates is performed by calculating total counts and evaluating the number of genes detected per sample. This process flags low-complexity libraries and sequencing issues, producing structured data and visual reports for data integrity verification.

You can run bulk RNA-seq QC locally without uploading data because the Skill performs privacy-first computation. It relies entirely on local dependencies like pandas, matplotlib, and numpy to process your count matrix and generate visual reports on your own machine.

Yes, this bulk RNA-seq QC tool works with a standard CSV count matrix. You can provide a file like 'counts.csv' as input, and the Skill will process it using pandas to assess sample correlation, gene detection, and library size distribution.

Evaluating sample-to-sample correlation during bulk RNA-seq quality control reveals batch effects and outlier samples. Visualizing this similarity ensures your count data is reliable, preventing downstream analysis errors caused by problematic samples before you begin differential expression.