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.
npx skills add majiayu000/claude-skill-registry --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.
# 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 ### Setup ```r library(limma) library(edgeR) library(splines) # Load count data counts <- read.table('counts.txt', header=TRUE, row.names=1) metadata <- read.table('metadata.txt', header=TRUE) # metadata should have: sample, time, condition, replicate ``` ### Basic Time-Series Model ```r # Create DGEList dge <- DGEList(counts=counts) dge <- calcNormFactors(dge) # Filter low counts keep <- filterByExpr(dge, group=metadata$condition) dge <- dge[keep, , keep.lib.sizes=FALSE] # Design with natural splines time <- metadata$time design <- model.matrix(~ ns(time, df=3)) # voom transformation v <- voom(dge, design, plot=TRUE) # Fit model fit <- lmFit(v, design) fit <- eBayes(fit) # Test for any temporal effect (all spline terms) results <- topTable(fit, coef=2:4, number=Inf) ``` ### Two Conditions Over Time ``
- 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
- Visualization
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --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 majiayu000/claude-skill-registry, a repository with 534 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.