Seurat — Core Workflow

Seurat is the dominant scRNA-seq R package. Master the full standard workflow on the PBMC3k dataset — QC filtering, SCTransform normalisation, PCA, UMAP, Leiden clustering, marker detection, and cell type annotation.

Weeks 32–33Timeline

~14 hrsStudy time

FREEAlways

Start Lesson 1 → View in curriculum →

What you'll learn

Topics covered in this module

QC metrics & filtering

SCTransform

PCA · UMAP

FindClusters

FindAllMarkers

Cell type annotation

Read10X · Seurat object

HVGs · ElbowPlot

DoHeatmap · FeaturePlot

Curriculum

10 lessons in this module

1Introduction to scRNA-seq & SeuratWhat is scRNA-seq, why Seurat, data types (10x, MEX format), install SeuratLive

2Loading & Inspecting DataRead10X(), CreateSeuratObject(), sparse matrices, cell/gene countsSoon

3Quality ControlnFeature_RNA, nCount_RNA, percent.mt, VlnPlot(), filtering cellsSoon

4NormalisationNormalizeData(), log-normalisation theory, why raw counts misleadSoon

5Feature SelectionFindVariableFeatures(), HVGs, VariableFeaturePlot()Soon

6Scaling & PCAScaleData(), RunPCA(), ElbowPlot(), choosing dimensionsSoon

7ClusteringFindNeighbors(), FindClusters(), resolution parameter, graph-based clusteringSoon

8UMAP & VisualisationRunUMAP(), DimPlot(), FeaturePlot(), VlnPlot() per clusterSoon

9Marker Gene IdentificationFindAllMarkers(), FindMarkers(), Wilcoxon test, DoHeatmap()Soon

10Cell Type Annotation & SavingManual annotation, RenameIdents(), saving with saveRDS(), capstoneSoon

📋 Not sure where this fits? Module 25 is part of the full bioinformatics curriculum — a structured 42-week learning path from Bash to single-cell RNA-seq.

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Lesson 1 is live. The rest are on their way.

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Module 25 of 31