Single-Cell RNA-seq Pipeline

Complete workflow from 10X Genomics Cell Ranger output to annotated cell types.

Workflow Overview

10X data (filtered_feature_bc_matrix) | v [1. Load Data] ---------> Read10X / read_10x_h5 | v [2. QC Filtering] ------> nFeature, percent.mt, doublets | v [3. Normalization] -----> SCTransform or LogNormalize | v [4. HVG Selection] -----> FindVariableFeatures | v [5. Dim Reduction] -----> PCA → UMAP | v [6. Clustering] --------> FindNeighbors → FindClusters | v [7. Markers] -----------> FindAllMarkers | v [8. Annotation] --------> Manual or automated | v Annotated Seurat/AnnData object

Primary Path: Seurat (R)

Step 1: Load 10X Data

library(Seurat) library(ggplot2) library(dplyr)

Load from Cell Ranger output

data_dir <- 'cellranger_output/filtered_feature_bc_matrix' counts <- Read10X(data.dir = data_dir)

Create Seurat object

seurat_obj <- CreateSeuratObject(counts = counts, project = 'my_project', min.cells = 3, min.features = 200)

Step 2: Quality Control

Calculate QC metrics

seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-') seurat_obj[['percent.ribo']] <- PercentageFeatureSet(seurat_obj, pattern = '^RP[SL]')

Visualize QC metrics

VlnPlot(seurat_obj, features = c('nFeature_RNA', 'nCount_RNA', 'percent.mt'), ncol = 3)

Filter cells

seurat_obj <- subset(seurat_obj, nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20 & nCount_RNA > 500)

cat('Cells after QC:', ncol(seurat_obj), '\n')

QC Checkpoint 1: Review QC plots

• Remove cells with very low/high gene counts

• Remove cells with high mitochondrial content (dying cells)

Step 3: Doublet Detection

library(scDblFinder)

Convert to SCE for scDblFinder

sce <- as.SingleCellExperiment(seurat_obj) sce <- scDblFinder(sce)

Add back to Seurat

seurat_obj$doublet_class <- sce$scDblFinder.class seurat_obj$doublet_score <- sce$scDblFinder.score

Remove doublets

seurat_obj <- subset(seurat_obj, doublet_class == 'singlet') cat('Cells after doublet removal:', ncol(seurat_obj), '\n')

Step 4: Normalization with SCTransform

SCTransform (recommended for most analyses)

seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE)

Alternative: Standard normalization

seurat_obj <- NormalizeData(seurat_obj) seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = 2000) seurat_obj <- ScaleData(seurat_obj, vars.to.regress = 'percent.mt')

Step 5: Dimensionality Reduction

PCA

seurat_obj <- RunPCA(seurat_obj, npcs = 50, verbose = FALSE)

Determine optimal PCs

ElbowPlot(seurat_obj, ndims = 50)

UMAP

n_pcs <- 30 # Choose based on elbow plot seurat_obj <- RunUMAP(seurat_obj, dims = 1:n_pcs, verbose = FALSE)

Step 6: Clustering

Find neighbors

seurat_obj <- FindNeighbors(seurat_obj, dims = 1:n_pcs, verbose = FALSE)

Find clusters (try multiple resolutions)

seurat_obj <- FindClusters(seurat_obj, resolution = c(0.2, 0.4, 0.6, 0.8, 1.0), verbose = FALSE)

Visualize

DimPlot(seurat_obj, reduction = 'umap', group.by = 'SCT_snn_res.0.4', label = TRUE)

QC Checkpoint 2: Assess clustering

• Clusters should be visually separable on UMAP

• Resolution 0.4-0.8 is often appropriate

Step 7: Find Marker Genes

Set identity to chosen resolution

Idents(seurat_obj) <- 'SCT_snn_res.0.4'

Find markers for all clusters

markers <- FindAllMarkers(seurat_obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

Top markers per cluster

top_markers <- markers %>% group_by(cluster) %>% slice_max(n = 10, order_by = avg_log2FC)

Visualize top markers

DoHeatmap(seurat_obj, features = top_markers$gene) + NoLegend()

Step 8: Cell Type Annotation

Manual annotation based on known markers

Example for PBMC data:

cluster_annotations <- c( '0' = 'CD4 T cells', '1' = 'CD14 Monocytes', '2' = 'B cells', '3' = 'CD8 T cells', '4' = 'NK cells', '5' = 'CD16 Monocytes', '6' = 'Dendritic cells' )

seurat_obj$cell_type <- cluster_annotations[as.character(Idents(seurat_obj))]

Final UMAP

DimPlot(seurat_obj, reduction = 'umap', group.by = 'cell_type', label = TRUE)

Save object

saveRDS(seurat_obj, 'seurat_annotated.rds')

Alternative Path: Scanpy (Python)

import scanpy as sc import numpy as np

Load 10X data

adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5') adata.var_names_make_unique()

QC metrics

adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)

Filter

sc.pp.filter_cells(adata, min_genes=200) sc.pp.filter_genes(adata, min_cells=3) adata = adata[adata.obs.n_genes_by_counts < 5000, :] adata = adata[adata.obs.pct_counts_mt < 20, :]

Doublet detection

sc.pp.scrublet(adata) adata = adata[~adata.obs['predicted_doublet'], :]

Normalize and HVGs

sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) sc.pp.highly_variable_genes(adata, n_top_genes=2000)

PCA, neighbors, UMAP

sc.pp.scale(adata, max_value=10) sc.tl.pca(adata, n_comps=50) sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30) sc.tl.umap(adata)

Clustering

sc.tl.leiden(adata, resolution=0.5)

Markers

sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon') sc.pl.rank_genes_groups(adata, n_genes=10, sharey=False)

Save

adata.write('scanpy_annotated.h5ad')

Parameter Recommendations

Step Parameter Recommendation

QC min.features 200-500

QC max.features 2500-5000 (depends on data)

QC percent.mt <10-20%

SCTransform vars.to.regress percent.mt

PCA npcs 30-50

UMAP dims 15-30 (check elbow plot)

Clustering resolution 0.4-0.8 (start with 0.5)

Troubleshooting

Issue Likely Cause Solution

All cells filtered QC too strict Relax thresholds

Poor UMAP separation Too few HVGs or PCs Increase nfeatures, check n_pcs

Too many/few clusters Wrong resolution Adjust resolution parameter

Unknown cell types Missing markers Check known marker genes manually

Complete R Workflow

library(Seurat) library(scDblFinder) library(ggplot2) library(dplyr)

Configuration

data_dir <- 'filtered_feature_bc_matrix' output_dir <- 'results' dir.create(output_dir, showWarnings = FALSE)

Load

counts <- Read10X(data.dir = data_dir) seurat_obj <- CreateSeuratObject(counts = counts, min.cells = 3, min.features = 200) cat('Initial cells:', ncol(seurat_obj), '\n')

QC

seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-') seurat_obj <- subset(seurat_obj, nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20) cat('After QC:', ncol(seurat_obj), '\n')

Doublets

sce <- as.SingleCellExperiment(seurat_obj) sce <- scDblFinder(sce) seurat_obj$doublet <- sce$scDblFinder.class seurat_obj <- subset(seurat_obj, doublet == 'singlet') cat('After doublet removal:', ncol(seurat_obj), '\n')

Normalize

seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE)

Dimension reduction

seurat_obj <- RunPCA(seurat_obj, npcs = 50, verbose = FALSE) seurat_obj <- RunUMAP(seurat_obj, dims = 1:30, verbose = FALSE)

Cluster

seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30, verbose = FALSE) seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = FALSE)

Markers

markers <- FindAllMarkers(seurat_obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) write.csv(markers, file.path(output_dir, 'markers.csv'))

Save

saveRDS(seurat_obj, file.path(output_dir, 'seurat_object.rds'))

Plots

pdf(file.path(output_dir, 'umap.pdf'), width = 10, height = 8) DimPlot(seurat_obj, reduction = 'umap', label = TRUE) dev.off()

cat('Pipeline complete. Object saved to:', output_dir, '\n')

Related Skills

• single-cell/data-io - Loading different formats

• single-cell/preprocessing - QC details

• single-cell/doublet-detection - Doublet methods comparison

• single-cell/clustering - Clustering parameters

• single-cell/markers-annotation - Annotation strategies

• single-cell/multimodal-integration - CITE-seq, multiome