/bio-differential-expression-timeseries-de

name: bio-differential-expression-timeseries-de
description: 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.
tool_type: r
primary_tool: limma

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 are significantly non-zero.

Setup

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

# 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

# Design for condition-specific time effects
condition <- factor(metadata$condition)
time <- metadata$time

# Interaction model
design <- model.matrix(~ condition * ns(time, df=3))

v <- voom(dge, design, plot=TRUE)
fit <- lmFit(v, design)
fit <- eBayes(fit)

# Genes with different temporal patterns between conditions
# Test interaction terms
results_interaction <- topTable(fit, coef=grep(':', colnames(design)), number=Inf)

Contrasts for Specific Comparisons

# Compare time points within condition
design <- model.matrix(~ 0 + condition:factor(time))
colnames(design) <- gsub(':', '_', colnames(design))

v <- voom(dge, design)
fit <- lmFit(v, design)

# Contrast: Treated_T2 vs Treated_T0
contrast <- makeContrasts(
    early_response = ConditionTreated_time2 - ConditionTreated_time0,
    late_response = ConditionTreated_time6 - ConditionTreated_time0,
    levels = design
)

fit2 <- contrasts.fit(fit, contrast)
fit2 <- eBayes(fit2)
results <- topTable(fit2, coef='early_response', number=Inf)

maSigPro

**Goal:** Identify genes with significant temporal expression profiles using two-step polynomial regression.

**Approach:** Apply global regression to find time-variable genes, then stepwise regression to refine significant profiles and cluster them.

Installation

BiocManager::install('maSigPro')

Two-Step Regression

library(maSigPro)

# Create experimental design
# Time, Replicate, Group columns required
edesign <- data.frame(
    Time = metadata$time,
    Replicate = metadata$replicate,
    Control = as.numeric(metadata$condition == 'Control'),
    Treatment = as.numeric(metadata$condition == 'Treatment')
)
rownames(edesign) <- metadata$sample

# Normalize counts
dge <- DGEList(counts=counts)
dge <- calcNormFactors(dge)
norm_counts <- cpm(dge, log=TRUE)

# Create design matrix for polynomial regression
design <- make.design.matrix(edesign, degree=3)

# Step 1: Global regression (find time-variable genes)
fit <- p.vector(norm_counts, design, Q=0.05, MT.adjust='BH')

# Step 2: Stepwise regression (find significant profiles)
tstep <- T.fit(fit, step.method='backward', alfa=0.05)

# Get significant genes
sigs <- get.siggenes(tstep, rsq=0.6, vars='groups')

# Visualize clusters
see.genes(sigs$sig.genes, show.fit=TRUE, dis=design$dis,
          cluster.method='hclust', k=9)

Cluster Visualization

# Plot specific clusters
pdf('timeseries_clusters.pdf', width=12, height=10)
see.genes(sigs$sig.genes, show.fit=TRUE, dis=design$dis,
          cluster.method='hclust', k=9,
          newX11=FALSE)
dev.off()

# Get genes per cluster
cluster_genes <- sigs$sig.genes$sig.profiles

ImpulseDE2

**Goal:** Detect genes with transient impulse-like expression patterns (rise then fall, or vice versa).

**Approach:** Fit sigmoid-based impulse models to each gene and test for significant temporal dynamics.

Installation

BiocManager::install('ImpulseDE2')

Run ImpulseDE2

library(ImpulseDE2)
library(DESeq2)

# Create annotation
dfAnnotation <- data.frame(
    Sample = colnames(counts),
    Time = metadata$time,
    Condition = metadata$condition,
    Batch = metadata$batch
)

# Run ImpulseDE2
impulse_results <- runImpulseDE2(
    matCountData = as.matrix(counts),
    dfAnnotation = dfAnnotation,
    boolCaseCtrl = TRUE,
    vecConfounders = c('Batch'),
    scaNProc = 4
)

# Get significant genes
sig_genes <- impulse_results$dfImpulseDE2Results[
    impulse_results$dfImpulseDE2Results$padj < 0.05, ]

DESeq2 Likelihood Ratio Test

**Goal:** Test for any temporal effect across discrete time points w