Bio Rna Quantification Count Matrix Qc Agent Skill
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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill bio-rna-quantification-count-matrix-qcWhat comes with it
7 630 bytes besides the instructionThe instruction itself
34 sections, as written by the authorVersion Compatibility
Reference examples tested with: DESeq2 1.42+, ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+, scipy 1.12+, seaborn 0.13+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Count Matrix QC
"Check my count matrix for outliers and batch effects" → Perform PCA, sample-sample correlation, library size assessment, and outlier detection before running differential expression.
- R:
DESeq2::vst()→plotPCA(), sample distance heatmap - Python:
sklearn.decomposition.PCA,seaborn.clustermap
Quality control and exploratory analysis of count matrices before differential expression.
Load and Inspect Counts
Goal: Assess count matrix quality before differential expression by detecting outliers, batch effects, and sample relationship problems.
Approach: Load counts into DESeq2 or pandas, compute per-sample library size statistics, apply variance-stabilizing transformation, then run PCA and sample-sample correlation to identify outliers and batch structure.
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
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
# Total counts per sample
colSums(counts(dds))
# Genes detected per sample
colSums(counts(dds) > 0)
# Counts summary
summary(colSums(counts(dds)))
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
# Remove genes with low counts across samples
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep, ]
# More stringent: at least N samples with count >= M
keep <- rowSums(counts(dds) >= 10) >= 3
dds <- dds[keep, ]
Python
min_counts = 10
min_samples = 3
gene_filter = (counts >= min_counts).sum(axis=1) >= min_samples
counts_filtered = counts[gene_filter]
Normalize for Visualization
R (DESeq2 VST)
# Variance stabilizing transformation
vsd <- vst(dds, blind = TRUE)
# Or regularized log (slower, better for small n)
rld <- rlog(dds, blind = TRUE)
# Get transformed values
vst_matrix <- assay(vsd)
Python (log2 CPM)
from sklearn.preprocessing import StandardScaler
cpm = counts * 1e6 / counts.sum()
log_cpm = np.log2(cpm + 1)
Sample Correlation
R
library(pheatmap)
# Sample correlation heatmap
sample_cor <- cor(assay(vsd))
pheatmap(sample_cor, annotation_col = coldata)
# Sample distance heatmap
sample_dist <- dist(t(assay(vsd)))
pheatmap(as.matrix(sample_dist), annotation_col = coldata)
Python
import seaborn as sns
import matplotlib.pyplot as plt
sample_cor = log_cpm.corr()
sns.clustermap(sample_cor, annot=True, cmap='RdBu_r', center=0.9,
vmin=0.8, vmax=1.0)
plt.savefig('sample_correlation.png')
PCA Analysis
R
# PCA plot
plotPCA(vsd, intgroup = 'condition')
# Custom PCA
pca <- prcomp(t(assay(vsd)))
pca_df <- data.frame(PC1 = pca$x[,1], PC2 = pca$x[,2],
condition = coldata$condition)
library(ggplot2)
ggplot(pca_df, aes(PC1, PC2, color = condition)) +
geom_point(size = 3) +
geom_text(aes(label = rownames(pca_df)), vjust = -0.5)
Python
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
pca_result = pca.fit_transform(log_cpm.T)
plt.figure(figsize=(8, 6))
for condition in metadata['condition'].unique():
mask = metadata['condition'] == condition
plt.scatter(pca_result[mask, 0], pca_result[mask, 1], label=condition)
plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.1%})')
plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.1%})')
plt.legend()
plt.savefig('pca_plot.png')
Detect Outliers
R
# Cook's distance (after DESeq)
dds <- DESeq(dds)
W <- results(dds)$cooksd
boxplot(W, main = "Cook's Distance")
# Identify outlier samples from PCA
pca <- prcomp(t(assay(vsd)))
outliers <- abs(scale(pca$x[,1])) > 3 | abs(scale(pca$x[,2])) > 3
Python
from scipy import stats
z_scores = stats.zscore(pca_result, axis=0)
outliers = (np.abs(z_scores) > 3).any(axis=1)
print('Potential outliers:', counts.columns[outliers].tolist())
Check for Batch Effects
R
# Color PCA by batch
plotPCA(vsd, intgroup = c('condition', 'batch'))
# Test for batch effect
design(dds) <- ~ batch + condition
dds <- DESeq(dds)
Python
# Color by batch in PCA
for batch in metadata['batch'].unique():
mask = metadata['batch'] == batch
plt.scatter(pca_result[mask, 0], pca_result[mask, 1],
marker=['o', 's', '^'][list(metadata['batch'].unique()).index(batch)],
label=f'Batch {batch}')
Library Complexity
R
# Genes detected vs library size
plot(colSums(counts(dds)), colSums(counts(dds) > 0),
xlab = 'Library Size', ylab = 'Genes Detected')
# Saturation check
Python
plt.scatter(counts.sum(), (counts > 0).sum())
plt.xlabel('Total Counts')
plt.ylabel('Genes Detected')
plt.savefig('library_complexity.png')
Gene-Level QC
R
# Most variable genes
rv <- rowVars(assay(vsd))
top_var <- order(rv, decreasing = TRUE)[1:500]
# Expression distribution
boxplot(log2(counts(dds) + 1), las = 2)
Python
gene_var = log_cpm.var(axis=1).sort_values(ascending=False)
top_var_genes = gene_var.head(500).index
counts[top_var_genes].boxplot(figsize=(12, 6))
plt.xticks(rotation=45)
plt.savefig('gene_expression_dist.png')
Summary Report
# Quick summary
cat('Samples:', ncol(dds), '\n')
cat('Genes before filter:', nrow(counts), '\n')
cat('Genes after filter:', nrow(dds), '\n')
cat('Median library size:', median(colSums(counts(dds))), '\n')
cat('Median genes detected:', median(colSums(counts(dds) > 0)), '\n')
Related Skills
- rna-quantification/featurecounts-counting - Generate counts
- rna-quantification/tximport-workflow - Import transcript counts
- differential-expression/de-visualization - Downstream visualization
- differential-expression/deseq2-basics - DE analysis
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How to use it
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