Introduction to scRNA-Seq Analysis
Single-cell RNA sequencing reveals cellular heterogeneity hidden in bulk samples. The analysis pipeline transforms raw counts into biologically meaningful clusters representing distinct cell types or states.
The Standard Workflow
1. Quality Control
Filter cells based on:
2. Normalization
LogNormalize in Seurat:
seurat_obj <- NormalizeData(seurat_obj,
normalization.method = "LogNormalize",
scale.factor = 10000)
3. Feature Selection
Identify highly variable genes (HVGs) that drive biological variation:
seurat_obj <- FindVariableFeatures(seurat_obj,
selection.method = "vst",
nfeatures = 2000)
4. Dimensionality Reduction
5. Clustering
Marker Gene Identification
Use FindAllMarkers to identify genes defining each cluster:
markers <- FindAllMarkers(seurat_obj,
only.pos = TRUE,
min.pct = 0.25,
logfc.threshold = 0.25)
