Timeseries De

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Five audit layers

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Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/differential-expression/timeseries-deThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for…

---
name: bio-differential-expression-timeseries-de
description: Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
tool_type: r
primary_tool: DESeq2
---

## Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, splines (base R), maSigPro 1.74+, ImpulseDE2 1.10+ (Bioconductor archive; verify availability), variancePartition / dream 1.32+, Mfuzz 2.62+, TCseq 1.26+, ggplot2 3.5+, pheatmap 1.0+

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to 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.

# Time-Series Differential Expression

**"Find genes that change over time"** -> Define what "change" means -- any non-zero time effect (LRT), a smooth nonlinear trend (splines), a transient impulse (ImpulseDE2), or differing trajectories between groups (interaction LRT) -- and choose the model that asks that specific question while handling repeated-measures correctly.

## The Single Most Important Modern Insight -- Most dedicated time-course tools UNDERPERFORM pairwise + LRT on short series

Spies, Renz, Beyer, Ciaudo 2019 *Brief Bioinform* 20:288 benchmarked dedicated time-course tools (ImpulseDE2, splineTC, maSigPro, EBSeqHMM, TimeReg) against naive DESeq2/edgeR pairwise comparisons + LRT for omnibus, on simulated and real time-courses. Finding: **on short series (<8 time points), naive pairwise pattern-of-significance OUTPERFORMS dedicated TC tools** because of high false-positive rates in the latter. The exception is ImpulseDE2, which holds up better than the others -- IF its impulse assumption (rise-then-plateau or fall-then-plateau) actually fits the biology.

For most experimental time courses (3-6 time points, common in pharmacology and developmental biology), the right tool is DESeq2 with `test='LRT'` and a sensible reduced model. Reserve splines for >5 evenly-spaced time points; reserve ImpulseDE2 for monotonic-then-saturating dynamics; reserve DREAM for repeated measures.

A second insight that is constantly violated: pseudoreplication. If 3 subjects each contribute 4 time points (12 samples), the effective sample size for testing TIME effects is closer to 3, not 12 -- the within-subject observations are not independent. Treating them as independent inflates type-I error dramatically. Either include subject as a fixed effect, use DREAM (mixed model), or collapse to per-subject means (loses time info).

## Algorithmic Taxonomy

| Method | What it tests | Best for | Failure mode |
|--------|---------------|----------|--------------|
| DESeq2 LRT (`test='LRT'`, `reduced=`) | Joint hypothesis: dropped terms are jointly zero | Default for "any time effect"; multi-group time interaction | Reports LFC of last coefficient, not omnibus -- use padj only |
| DESeq2 + splines (`ns(time, df=3)`) | Smooth nonlinear time effect | 5+ time points, smooth dynamics, multi-group interaction | Spline df > unique time points / 2 overfits |
| maSigPro (Nueda 2014 RNA-seq update) | Polynomial regression on time per group | Multi-group time-course, regression-style hypotheses | Polynomial assumption can be wrong; less popular than DESeq2 LRT |
| ImpulseDE2 (Fischer, Theis, Yosef 2018) | Constant vs monotonic vs impulse trajectories | Monotonic-then-saturating or impulse-like responses | Fails on oscillatory, multi-phase, monotonic-non-asymptotic; sensitive to noise on short series |
| DREAM (Hoffman, Roussos 2021) | Per-gene linear mixed model with random subject | Repeated measures with >2 time points per subject | Slower; requires variancePartition stack; `ddf='adaptive'` default (Kenward-Roger for n<=20, Satterthwaite otherwise) |
| voom + duplicateCorrelation | Single average within-subject correlation | Technical reps within bio reps, paired pre/post | Single correlation across all genes is approximation |
| edgeR LRT with subject as factor | Subject as fixed effect | Small subject count, simple design | Wastes df; can't handle continuous time well |
| TCseq | Spline-based DE + fuzzy clustering | Combined pipeline for DE + cluster discovery | Single-tool dependency; verify maintenance |

## Decision Tree by Scenario

| Scenario | Recommended approach | Why |
|----------|---------------------|-----|
| 2-3 discrete time points, independent samples per time | DESeq2 LRT, time as factor, `reduced = ~1` | Too few points for splines |
| 5+ time points, single group, smooth dynamics | DESeq2 LRT with `ns(time, df=3)`, `reduced = ~1` | Splines capture nonlinearity efficiently |
| 5+ time points, two groups, "do trajectories differ?" | LRT with `~ treatment * ns(time, df=3)` vs `reduced = ~ treatment + ns(time, df=3)` | Tests the interaction (treatment-specific time response) |
| Same subject sampled over time (longitudinal) | DREAM (random subject) OR DESeq2 with subject fixed effect | Mandatory: pseudoreplication otherwise |
| Monotonic-then-saturating biology expected | ImpulseDE2 | Built for the assumption |
| Circadian or cell-cycle (cyclical) | Fourier basis (`fda::create.fourier.basis`) or dedicated tools (JTK_CYCLE, MetaCycle) | Splines can't represent perio

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Timeseries De

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