Adds decision logic for choosing quantification paths (STAR vs Salmon), strandedness determination, and DESeq2 modeling pitfalls that LLMs routinely get wrong.
Usage
Activate when choosing between alignment-based (STAR+featureCounts) vs alignment-free (Salmon) quantification
Activate when setting up DESeq2 from either count source
Activate when debugging unexpected low counts or weak DE signal
Core Concepts
Decision Logic
Need BAMs? (variant calling, fusions, novel transcripts)
├── YES → STAR two-pass + featureCounts
└── NO → Salmon (faster, lower RAM, bias-corrected)
Salmon output → DESeq2:
└── Use DESeqDataSetFromTximport (preserves length offset)
└── NEVER round txi$counts and pass to DESeqDataSetFromMatrix
Filtering strategy (VQSR equivalent):
├── ≥30 samples → VQSR-like (default DESeq2 shrinkage works well)
└── <30 samples → still works, but pre-filter aggressively
Strandedness determination (MUST verify before counting):
Kit
featureCounts -s
Salmon -l
TruSeq Stranded / dUTP
2
ISR (PE) / SR (SE)
Ligation / forward
1
ISF / SF
Unstranded
0
IU / U
Verify with: infer_experiment.py (RSeQC) or Salmon's lib_format_counts.json.
Critical Parameters
Parameter
Value
When to change
--sjdbOverhang
readLength - 1 (default 100)
Non-100bp reads
--twopassMode Basic
Always for DE
Skip only for speed in QC runs
Salmon --gcBias --seqBias
Always on
Never skip for Illumina
Salmon --validateMappings
Always on
Enables selective alignment
featureCounts -s
Kit-dependent
ALWAYS verify empirically
DESeq2 pre-filter
rowSums(counts >= 10) >= smallest_group
Adjust 10 for shallow sequencing
lfcShrink type
"apeglm"
Use "ashr" for arbitrary contrasts
Design formula
~ batch + condition (interest LAST)
Always put interest term last
Common Mistakes
Wrong: Guessing strandedness (-s) instead of verifying empirically
Right: Check with infer_experiment.py or Salmon lib_format_counts.json before running featureCounts
Why: Wrong strandedness causes 2–10× lower counts and weak DE signal
Wrong: Passing normalized counts (TPM, FPKM, CPM) to DESeq2
Right: Always start from raw integer counts
Why: Normalized values break the negative-binomial model
Wrong: Rounding txi$counts and passing to DESeqDataSetFromMatrixRight: Use DESeqDataSetFromTximport(txi, ...) which preserves the length offset
Why: Rounding discards per-gene length correction for transcript-length bias
Wrong: Placing variable of interest first in design (~ condition + batch)
Right: Put interest last: ~ batch + conditionWhy: DESeq2 tests the last term by default
Wrong: Assuming DESeq2 reorders samples to match
Right: Assert stopifnot(all(colnames(cts) == rownames(coldata))) before creating DESeq object
Why: DESeq2 silently mislabels samples if order doesn't match
Wrong: Skipping pre-filtering of low-count genes
Right: Filter with rowSums(counts(dds) >= 10) >= smallest_group_sizeWhy: Near-zero genes inflate runtime and dilute multiple-testing correction
Wrong: Using STAR ReadsPerGene.out.tab column 2 for stranded libraries
Right: Use column 3 (forward) or column 4 (reverse) matching your kit
Why: Column 2 is unstranded; using it for stranded data halves effective counts
Wrong: Including samples with <10M mapped reads or <70% mapping rate
Right: Exclude or flag before fitting
Why: Low-quality samples distort size factors and dispersion estimates
Wrong: Modeling when batch is perfectly confounded with condition
Right: Check table(coldata$batch, coldata$condition) first
No statistical model can separate perfectly correlated effects
Response Format
Lead with the command or code the user needs — explain after
