Bio Single Cell Data Io

Version Compatibility

Reference examples tested with: Cell Ranger 8.0+, anndata 0.10+, numpy 1.26+, pandas 2.2+, 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 Data I/O

Read, write, and create single-cell data objects for analysis.

Scanpy (Python)

Goal: Load, create, and save single-cell data objects using Scanpy and AnnData.

Approach: Read 10X Genomics output, CSV, or Loom formats into AnnData objects, manipulate metadata and layers, and write to h5ad format.

"Load my 10X data" → Read Cell Ranger output directory or h5 file into an AnnData object with expression matrix, cell barcodes, and gene annotations.

Required Imports

import scanpy as sc
import anndata as ad
import pandas as pd
import numpy as np

Reading 10X Genomics Data

# Read 10X cellranger output (filtered_feature_bc_matrix directory)
adata = sc.read_10x_mtx('filtered_feature_bc_matrix/', var_names='gene_symbols', cache=True)
print(f'Loaded {adata.n_obs} cells x {adata.n_vars} genes')

# Read 10X h5 file directly
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')

AnnData Object Structure

# AnnData stores:
# - adata.X: expression matrix (cells x genes)
# - adata.obs: cell metadata (DataFrame)
# - adata.var: gene metadata (DataFrame)
# - adata.uns: unstructured annotations (dict)
# - adata.obsm: cell embeddings (PCA, UMAP)
# - adata.varm: gene embeddings
# - adata.obsp: cell-cell graphs
# - adata.layers: alternative matrices (raw counts, normalized)

print(f'Shape: {adata.shape}')
print(f'Cell metadata: {adata.obs.columns.tolist()}')
print(f'Gene metadata: {adata.var.columns.tolist()}')

Creating AnnData from Matrix

import anndata as ad
import numpy as np
import pandas as pd

counts = np.random.poisson(1, size=(100, 500))  # 100 cells x 500 genes
cell_ids = [f'cell_{i}' for i in range(100)]
gene_ids = [f'gene_{i}' for i in range(500)]

adata = ad.AnnData(
    X=counts,
    obs=pd.DataFrame(index=cell_ids),
    var=pd.DataFrame(index=gene_ids)
)

Reading/Writing h5ad Files

# h5ad is the native AnnData format
adata = sc.read_h5ad('data.h5ad')

# Write to h5ad
adata.write_h5ad('output.h5ad')

# Write compressed
adata.write_h5ad('output.h5ad', compression='gzip')

Reading Other Formats

# CSV/TSV (genes as columns, cells as rows)
adata = sc.read_csv('counts.csv')

# Loom format
adata = sc.read_loom('data.loom')

# Text file (tab-separated)
adata = sc.read_text('counts.txt')

Adding Metadata

# Add cell metadata
adata.obs['sample'] = 'sample_1'
adata.obs['batch'] = ['batch_1'] * 50 + ['batch_2'] * 50

# Add gene metadata
adata.var['gene_type'] = 'protein_coding'

# Add unstructured data
adata.uns['experiment'] = 'PBMC_3k'

Subsetting AnnData

# Subset by cells
adata_subset = adata[adata.obs['batch'] == 'batch_1'].copy()

# Subset by genes
adata_subset = adata[:, adata.var['highly_variable']].copy()

# Boolean indexing
adata_subset = adata[adata.obs['n_genes'] > 200, :].copy()

Storing Raw Counts

# Store raw counts before normalization
adata.raw = adata.copy()

# Access raw counts later
raw_counts = adata.raw.X

# Or use layers
adata.layers['counts'] = adata.X.copy()

Seurat (R)

Goal: Load, create, and save single-cell data objects using Seurat.

Approach: Read 10X Genomics output into Seurat objects, manipulate metadata, merge samples, and serialize with RDS or h5Seurat formats.

Required Libraries

library(Seurat)
library(Matrix)

Reading 10X Genomics Data

# Read 10X cellranger output
counts <- Read10X(data.dir = 'filtered_feature_bc_matrix/')

# Create Seurat object
seurat_obj <- CreateSeuratObject(counts = counts, project = 'PBMC', min.cells = 3, min.features = 200)
print(seurat_obj)

Reading 10X h5 File

# Read h5 file directly
counts <- Read10X_h5('filtered_feature_bc_matrix.h5')
seurat_obj <- CreateSeuratObject(counts = counts, project = 'PBMC')

Seurat Object Structure (v5)

# Seurat v5 uses layers instead of slots
# - Layers: counts, data, scale.data
# - Metadata: seurat_obj@meta.data
# - Reductions: seurat_obj@reductions
# - Graphs: seurat_obj@graphs

# Access layers (v5 syntax)
counts <- LayerData(seurat_obj, layer = 'counts')
# Or shorthand
counts <- seurat_obj[['RNA']]$counts

# Access metadata
head(seurat_obj@meta.data)

Creating from Matrix

# Create from sparse matrix
counts <- Matrix(rpois(1000 * 500, 1), nrow = 500, ncol = 1000, sparse = TRUE)
rownames(counts) <- paste0('gene_', 1:500)
colnames(counts) <- paste0('cell_', 1:1000)

seurat_obj <- CreateSeuratObject(counts = counts, project = 'MyProject')

Reading/Writing RDS Files

# Save Seurat object
saveRDS(seurat_obj, file = 'seurat_obj.rds')

# Load Seurat object
seurat_obj <- readRDS('seurat_obj.rds')

Adding Metadata

# Add cell metadata
seurat_obj$sample <- 'sample_1'
seurat_obj$batch <- c(rep('batch_1', 500), rep('batch_2', 500))

# Or using AddMetaData
metadata_df <- data.frame(
    cell_type = rep('unknown', ncol(seurat_obj)),
    row.names = colnames(seurat_obj)
)
seurat_obj <- AddMetaData(seurat_obj, metadata = metadata_df)

Subsetting Seurat Objects

# Subset by metadata
seurat_subset <- subset(seurat_obj, subset = batch == 'batch_1')

# Subset by cells
seurat_subset <- subset(seurat_obj, cells = colnames(seurat_obj)[1:500])

# Subset by features
seurat_subset <- subset(seurat_obj, features = rownames(seurat_obj)[1:100])

Merging Objects

# Merge multiple Seurat objects
merged <- merge(seurat_obj1, y = c(seurat_obj2, seurat_obj3), add.cell.ids = c('S1', 'S2', 'S3'))

# Join layers after merge (v5)
merged <- JoinLayers(merged)

Format Conversion

Goal: Convert single-cell data objects between Seurat (R) and AnnData (Python) formats.

Approach: Use SeuratDisk as an intermediary to convert via h5Seurat/h5ad bridge files.

Seurat to AnnData

# In R: save as h5Seurat
library(SeuratDisk)
SaveH5Seurat(seurat_obj, filename = 'data.h5seurat')
Convert('data.h5seurat', dest = 'h5ad')

# In Python: read converted file
adata = sc.read_h5ad('data.h5ad')

AnnData to Seurat

# In Python: save as h5ad
adata.write_h5ad('data.h5ad')

# In R: convert and load
library(SeuratDisk)
Convert('data.h5ad', dest = 'h5seurat')
seurat_obj <- LoadH5Seurat('data.h5seurat')

Common Data Formats

Related Skills

• preprocessing - QC filtering and normalization after loading
• clustering - Dimensionality reduction and clustering
• markers-annotation - Find marker genes and annotate cell types