bio-differential-expression-timeseries-de
Analyze time-series RNA-seq data using limma voom with splines, maSigPro, and ImpulseDE2. Identify genes with dynamic expression patterns. Use when analyzing time-series or longitudinal expression data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill timeseries-de --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.
## Version Compatibility Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, ggplot2 3.5+, limma 3.58+, scanpy 1.10+ 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 Identify genes with significant temporal expression patterns in time-course experiments. ## Approaches | Method | Best For | |--------|----------| | limma with splines | Smooth temporal patterns | | maSigPro | Multiple time points, regression | | ImpulseDE2 | Impulse-like patterns | | DESeq2 LRT | Discrete time comparisons | ## limma with Splines **Goal:** Identify genes with smooth temporal expression patterns using flexible spline models. **Approach:** Fit voom-transformed counts with natural spline basis functions in limma, testing spline coefficients for significance. **"Find genes that change over time in my RNA-seq experiment"** → Model temporal expression using spline regression and test whether spline terms
- Version Compatibility
- Approaches
- limma with Splines
- Setup
- Basic Time-Series Model
- Two Conditions Over Time
- Contrasts for Specific Comparisons
- maSigPro
- Installation
- Two-Step Regression
- Cluster Visualization
- ImpulseDE2
- Run ImpulseDE2
- DESeq2 Likelihood Ratio Test
What does the bio-differential-expression-timeseries-de skill do?
Analyze time-series RNA-seq data using limma voom with splines, maSigPro, and ImpulseDE2. Identify genes with dynamic expression patterns. Use when analyzing time-series or longitudinal expression data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill timeseries-de --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.