Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
Version Compatibility
Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+, scanpy 1.10+
Before using code patterns, verify installed versions match. If versions differ:
Python: pip show <package> then help(module.function) to check signatures
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.
Single-Cell Preprocessing
"Preprocess my scRNA-seq data" → Filter low-quality cells/genes, normalize counts, identify highly variable genes, and prepare data for dimensionality reduction and clustering.
Python: scanpy.pp.filter_cells() → normalize_total() → log1p() → highly_variable_genes()
R: Seurat::NormalizeData() → FindVariableFeatures() → ScaleData()
Quality control, filtering, normalization, and feature selection for scRNA-seq data.
Scanpy (Python)
Goal: Preprocess scRNA-seq data through QC filtering, normalization, and feature selection using Scanpy.
Approach: Calculate per-cell quality metrics, filter low-quality cells/genes, normalize library sizes, identify highly variable genes, and scale for downstream analysis.
Required Imports
import scanpy as sc
import numpy as np
Calculate QC Metrics
# Calculate mitochondrial gene percentage
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)
# Key metrics added to adata.obs:
# - n_genes_by_counts: genes detected per cell
# - total_counts: total UMI counts per cell
# - pct_counts_mt: percentage mitochondrial
Visualize QC Metrics import matplotlib.pyplot as plt
sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'], jitter=0.4, multi_panel=True)
sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt')
sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts')
Filter Cells and Genes # Filter cells by QC metrics
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_cells(adata, max_genes=5000)
# Filter by mitochondrial percentage
adata = adata[adata.obs['pct_counts_mt'] < 20, :].copy()
# Filter genes
sc.pp.filter_genes(adata, min_cells=3)
print(f'After filtering: {adata.n_obs} cells, {adata.n_vars} genes')
Store Raw Counts # Store raw counts before normalization
adata.raw = adata.copy()
# Or use layers
adata.layers['counts'] = adata.X.copy()
Normalization # Library size normalization (normalize to 10,000 counts per cell)
sc.pp.normalize_total(adata, target_sum=1e4)
# Log transform
sc.pp.log1p(adata)
Highly Variable Genes # Identify highly variable genes (default: top 2000)
sc.pp.highly_variable_genes(adata, n_top_genes=2000, flavor='seurat_v3', layer='counts')
# Visualize
sc.pl.highly_variable_genes(adata)
# Check results
print(f'Highly variable genes: {adata.var.highly_variable.sum()}')
Subset to HVGs (Optional) # Keep only highly variable genes for downstream analysis
adata_hvg = adata[:, adata.var.highly_variable].copy()
Scaling (Z-score) # Scale to unit variance and zero mean
sc.pp.scale(adata, max_value=10)
Regress Out Confounders # Regress out unwanted variation (e.g., cell cycle, mitochondrial)
sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])
Complete Preprocessing Pipeline Goal: Run end-to-end preprocessing from raw 10X counts to analysis-ready data.
Approach: Chain QC, filtering, normalization, HVG selection, and scaling into a single pipeline.
import scanpy as sc
adata = sc.read_10x_mtx('filtered_feature_bc_matrix/')
# QC
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
# Filter
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata = adata[adata.obs['pct_counts_mt'] < 20, :].copy()
# Store raw
adata.raw = adata.copy()
# Normalize
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
# HVGs
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
# Scale
adata = adata[:, adata.var.highly_variable].copy()
sc.pp.scale(adata, max_value=10)
Seurat (R) Goal: Preprocess scRNA-seq data through QC filtering, normalization, and feature selection using Seurat.
Approach: Calculate mitochondrial percentages, filter cells by QC thresholds, normalize with log or SCTransform, identify variable features, and scale for PCA.
Required Libraries library(Seurat)
library(ggplot2)
Calculate QC Metrics # Calculate mitochondrial percentage
seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-')
# View QC metrics
head([email protected] )
Visualize QC Metrics # Violin plots
VlnPlot(seurat_obj, features = c('nFeature_RNA', 'nCount_RNA', 'percent.mt'), ncol = 3)
# Scatter plots
plot1 <- FeatureScatter(seurat_obj, feature1 = 'nCount_RNA', feature2 = 'percent.mt')
plot2 <- FeatureScatter(seurat_obj, feature1 = 'nCount_RNA', feature2 = 'nFeature_RNA')
plot1 + plot2
Filter Cells # Filter by QC metrics
seurat_obj <- subset(seurat_obj,
subset = nFeature_RNA > 200 &
nFeature_RNA < 5000 &
percent.mt < 20)
cat('After filtering:', ncol(seurat_obj), 'cells\n')
Normalization (Log Normalization) # Standard log normalization
seurat_obj <- NormalizeData(seurat_obj, normalization.method = 'LogNormalize', scale.factor = 10000)
Normalization (SCTransform) # SCTransform - recommended for most workflows
# Combines normalization, scaling, and HVG selection
seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE)
Find Variable Features # Identify highly variable features (if not using SCTransform)
seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = 2000)
# Visualize
top10 <- head(VariableFeatures(seurat_obj), 10)
plot1 <- VariableFeaturePlot(seurat_obj)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot2
Scaling # Scale data (if not using SCTransform)
all.genes <- rownames(seurat_obj)
seurat_obj <- ScaleData(seurat_obj, features = all.genes)
# Or scale only variable features (faster)
seurat_obj <- ScaleData(seurat_obj)
Regress Out Confounders # Regress out unwanted variation during scaling
seurat_obj <- ScaleData(seurat_obj, vars.to.regress = c('percent.mt', 'nCount_RNA'))
Complete Preprocessing Pipeline (Log Normalization) Goal: Run end-to-end Seurat preprocessing with standard log normalization.
Approach: Load 10X data, compute QC metrics, filter, normalize with LogNormalize, select variable features, and scale.
library(Seurat)
counts <- Read10X(data.dir = 'filtered_feature_bc_matrix/')
seurat_obj <- CreateSeuratObject(counts = counts, min.cells = 3, min.features = 200)
# QC
seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-')
# Filter
seurat_obj <- subset(seurat_obj,
subset = nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20)
# Normalize
seurat_obj <- NormalizeData(seurat_obj)
# HVGs
seurat_obj <- FindVariableFeatures(seurat_obj, nfeatures = 2000)
# Scale
seurat_obj <- ScaleData(seurat_obj)
Complete Preprocessing Pipeline (SCTransform) Goal: Run end-to-end Seurat preprocessing with SCTransform for variance-stabilized normalization.
Approach: Load 10X data, compute QC metrics, filter, and apply SCTransform which jointly normalizes, selects HVGs, and scales.
library(Seurat)
counts <- Read10X(data.dir = 'filtered_feature_bc_matrix/')
seurat_obj <- CreateSeuratObject(counts = counts, min.cells = 3, min.features = 200)
# QC
seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-')
# Filter
seurat_obj <- subset(seurat_obj,
subset = nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20)
# SCTransform (does normalization, HVG, and scaling)
seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE)
QC Thresholds Reference Metric Typical Range Notes min_genes 200-500 Remove empty droplets max_genes 2500-5000 Remove doublets max_mt 5-20% Remove dying cells (tissue-dependent) min_cells 3-10 Remove rarely detected genes
Method Comparison Step Scanpy Seurat (Standard) Seurat (SCTransform) Normalize normalize_total + log1pNormalizeDataSCTransformHVGs highly_variable_genesFindVariableFeatures(included) Scale scaleScaleData(included) Regress regress_outScaleData(vars.to.regress)SCTransform(vars.to.regress)
Related Skills
data-io - Load data before preprocessing
clustering - PCA and clustering after preprocessing
markers-annotation - Find markers after clustering
