Bio Rna Quantification Count Matrix Qc

Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, ggplot2 3.5+, pheatmap 1.0+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+, seaborn 0.13+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• Python: pip show <package> then help(module.function) to check signatures

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" -> Assess depth, composition, sample relationships, and outliers on appropriately transformed data, then decide what (if anything) to remove or model before differential expression.

• R: DESeq2::vst() -> plotPCA(), sample-distance heatmap, Cook's distance
• Python: sklearn.decomposition.PCA, seaborn.clustermap (with the low-count caveat below)

Two principles govern this whole skill. First, DE testing runs on raw counts with a size-factor offset; the transformed matrices here are for QC and visualization only, never fed back into the count model. Second, raw counts confound depth, composition, and biology, so QC must look at the right scale: a variance-stabilized matrix for clustering/PCA, and the size factors and Cook's distances from the count model for normalization and outliers.

Load and Inspect

Goal: Get counts into a model object and read off depth and detection per sample.

Approach: Build a DESeqDataSet (from tximport or a matrix), then summarize library size and genes detected.

library(DESeq2)
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)

colSums(counts(dds))        # library size per sample
colSums(counts(dds) > 0)    # genes detected per sample

import pandas as pd, numpy as np
counts = pd.read_csv('count_matrix.csv', index_col=0)
metadata = pd.read_csv('sample_info.csv', index_col=0)
print(counts.sum()); print((counts > 0).sum())

Filtering: the principled cut

Goal: Drop genes with too little signal to test, in a depth- and design-aware way.

Approach: Prefer edgeR filterByExpr (keeps genes with enough counts in at least the smallest group's worth of samples) over an arbitrary CPM > 1 rule.

library(edgeR)
keep <- filterByExpr(counts(dds), group = dds$condition)
dds <- dds[keep, ]

min_counts, min_samples = 10, 3   # 10 reads in >=3 samples; ~smallest group size
counts_filt = counts[(counts >= min_counts).sum(axis=1) >= min_samples]

In DESeq2, pre-filtering is mainly for speed and to drop all-zero rows; the inferential filter is independent filtering done automatically inside results() (it picks a mean-count threshold maximizing discoveries at the chosen alpha). Keep pre-filtering light. For edgeR/limma-voom, filterByExpr is the filter.

Normalization and transformation

Composition bias is the reason depth scaling is not enough: if a few genes dominate a library, every other gene looks depressed at unchanged absolute output. DESeq2 median-of-ratios and edgeR TMM each estimate one size factor per sample assuming most genes are not DE, then apply it as an offset on the raw counts. CPM and TPM do NOT correct composition (they rescale by a within-sample total) -- the same reason TPM is invalid for cross-sample comparison upstream -- so they are for visualization, not DE normalization. For matrices with many structural zeros (single-cell, metagenomics), use the poscounts size-factor estimator.

For QC visualization the matrix must be homoskedastic. log2(CPM + 1) is not: at low counts the log amplifies sampling noise, so PCA on it is driven by noisy near-zero genes. Use a variance-stabilizing transform instead.

vsd <- vst(dds, blind = TRUE)    # blind=TRUE for unsupervised QC; FALSE only after DESeq() for plotting
mat <- assay(vsd)

PCA and sample relationships

Goal: See whether replicates cluster and whether PC1 is biology or a technical artifact.

Approach: PCA on the VST matrix (top variable genes), then read PC1 against depth and batch.

plotPCA(vsd, intgroup = 'condition')                     # uses top 500 most-variable genes
sampleDists <- dist(t(assay(vsd)))
pheatmap::pheatmap(as.matrix(sampleDists))

from sklearn.decomposition import PCA
# log-CPM PCA is a quick look only: low-count heteroskedasticity can drive the PCs.
# For publication QC, compute VST in R and bring the matrix into Python.
cpm = counts_filt * 1e6 / counts_filt.sum()
log_cpm = np.log2(cpm + 1)
pcs = PCA(n_components=2).fit_transform(log_cpm.T)

If PC1 correlates with library size or detected-gene count rather than condition, it is a depth artifact (color the PCA by log10 library size to confirm). A common pattern is PC1 = batch, PC2 = condition, which is a design problem, not a normalization fix.

Outlier detection with Cook's distance

Goal: Distinguish a single bad count in one gene from a globally bad sample.

Approach: Read per-gene-per-sample Cook's distances from the fitted model; treat single-gene outliers and whole-sample outliers differently.

dds <- DESeq(dds)
cooks <- assays(dds)[['cooks']]          # per gene x sample; NOT results(dds)$cooksd
boxplot(log10(cooks), las = 2, main = "Cook's distance")
# results() flags a gene whose max Cook's exceeds qf(0.99, p, m-p) by setting its p-value to NA.
# With >= 7 replicates per group (minReplicatesForReplace) DESeq2 replaces the outlier count instead.

A single-gene-in-one-sample outlier is exactly what Cook's filtering and replaceOutliers are for; let DESeq2 handle it. A whole-sample outlier (many flagged genes in one sample, that sample far on the VST-PCA, low correlation to its replicates, an anomalous size factor) is not rescuable by replaceOutliers. Investigate, and remove only with a documented technical cause, since post-hoc cherry-picking inflates false positives.

Batch effects

Known batch goes in the design; the engine estimates and removes it on raw counts while propagating uncertainty:

design(dds) <- ~ batch + condition       # condition last = contrast of interest

Do NOT run removeBatchEffect() or ComBat and feed the adjusted matrix into DESeq2/edgeR; those engines model batch internally, and pre-adjusting double-corrects and breaks the count model. limma::removeBatchEffect(assay(vsd), batch = vsd$batch) is for visualization only. For unknown/unmeasured structure, estimate surrogate variables (sva/svaseq) or factors of unwanted variation (RUVSeq: RUVg control genes, RUVs replicate samples, RUVr residuals) and add them to the design.

The fatal case: if batch is correlated with condition, regressing it out removes biology too; a perfect confound (all treated in batch 1, all control in batch 2) is statistically unfixable. Cross-tabulate batch against condition before fitting.

Library-level QC and sample swaps

sf <- sizeFactors(estimateSizeFactors(dds))   # a size factor far from 1 (< ~0.3 or > ~3) is a red flag

Do not deduplicate standard RNA-seq: high duplication is expected from highly expressed genes, and position-based dedup discards real signal (deduplicate only with UMIs). Screen for sample swaps cheaply with sex-linked genes (XIST high in XX; RPS4Y1/UTY/DDX3Y high in XY) against recorded sex, and confirm identity with genotype concordance tools (VerifyBamID, somalier) when available.

Red flags that should halt a DE analysis

1. A sample clusters away from its group on the VST-PCA (and concentrates Cook's-flagged genes).
2. A size factor far from 1, or a library an order of magnitude off the cohort.
3. Near-zero correlation of a sample to its replicates.
4. Condition (near-)perfectly confounded with batch, lane, or run.

Common Errors

Related Skills

• rna-quantification/featurecounts-counting - Generate the count matrix
• rna-quantification/tximport-workflow - Import transcript counts with the length offset
• differential-expression/deseq2-basics - DE testing after QC
• differential-expression/de-visualization - Downstream result visualization
• read-qc/rnaseq-qc - Upstream read-level QC (rRNA, degradation, contamination)

References

• Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15(12):550. doi:10.1186/s13059-014-0550-8
• Robinson MD, Oshlack A. 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11(3):R25. doi:10.1186/gb-2010-11-3-r25
• Risso D, Ngai J, Speed TP, Dudoit S. 2014. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 32(9):896-902. doi:10.1038/nbt.2931