bio-rna-quantification-count-matrix-qc
Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.
npx skills add majiayu000/claude-skill-registry --skill count-matrix-qc --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Count Matrix QC Quality control and exploratory analysis of count matrices before differential expression. ## Load and Inspect Counts ### R ```r library(DESeq2) # From tximport dds <- DESeqDataSetFromTximport(txi, colData = coldata, design = ~ condition) # From count matrix counts <- read.csv('count_matrix.csv', row.names = 1) coldata <- data.frame(condition = factor(c('ctrl', 'ctrl', 'treat', 'treat')), row.names = colnames(counts)) dds <- DESeqDataSetFromMatrix(countData = counts, colData = coldata, design = ~ condition) ``` ### Python ```python import pandas as pd import numpy as np counts = pd.read_csv('count_matrix.csv', index_col=0) metadata = pd.read_csv('sample_info.csv', index_col=0) ``` ## Basic Statistics ### R ```r # Total counts per sample colSums(counts(dds)) # Genes detected per sample colSums(counts(dds) > 0) # Counts summary summary(colSums(counts(dds))) ``` ### Python ```python total_counts = counts.sum() genes_detected = (counts > 0).sum() print('Total counts per sample:') print(total_counts) print('\nGenes detected:') print(genes_detected) ``` ## Filter Low-Count Genes ### R ```r # Remove genes with low counts across samples keep <- rowSums(counts(dds)) >= 10
- Load and Inspect Counts
- R
- Python
- Basic Statistics
- Filter Low-Count Genes
- Normalize for Visualization
- R (DESeq2 VST)
- Python (log2 CPM)
- Sample Correlation
- PCA Analysis
- Detect Outliers
- Check for Batch Effects
- Library Complexity
- Gene-Level QC
What does the bio-rna-quantification-count-matrix-qc skill do?
Quality control and exploration of RNA-seq count matrices before differential expression. Check for outliers, batch effects, and sample relationships. Use when assessing count matrix quality before DE analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill count-matrix-qc --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.