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09 — Single-Cell RNA-seq#

Scale transcriptome analysis to individual cells using droplet-based single-cell RNA sequencing (scRNA-seq). This workflow demonstrates a high-throughput pipeline for processing thousands of cells per sample, including barcode demultiplexing, quantification, and downstream clustering.

Concepts Covered

  • Droplet-based scRNA-seq processing (10x Genomics style)
  • Custom rule templates for repetitive preprocessing
  • High-concurrency scatter across cell barcodes
  • Resource-intensive alignment with splice-aware aligners
  • Integration with specialized scRNA-seq R/Python environments

Pipeline Overview#

graph TD
    A[cellranger_count] --> B[clustering_analysis]
    B --> C[trajectory_inference]
    C --> D[generate_sc_report]

Steps:

  1. Quantification — Align reads to transcriptome and count UMI/barcodes (e.g., CellRanger)
  2. Analysis — Quality control, normalization, and cell clustering (e.g., Seurat/Scanpy)
  3. Inference — Developmental trajectory and cell type identification
  4. Report — Generate an interactive single-cell analysis report

Workflow Definition#

# examples/gallery/09_single_cell_rnaseq.oxoflow

[workflow]
name = "sc-rnaseq-pipeline"
version = "1.0.0"
description = "Single-cell RNA-seq pipeline: CellRanger + Seurat"
author = "oxo-flow examples"

[config]
reference = "/data/references/GRCh38/cellranger_index"
samples = "sc_samples.csv"

[defaults]
threads = 8
memory = "32G"

[[rules]]
name = "cellranger_count"
input = ["raw/{sample}_R1.fastq.gz", "raw/{sample}_R2.fastq.gz"]
output = ["counts/{sample}/outs/filtered_feature_bc_matrix.h5"]
threads = 16
memory = "64G"
description = "scRNA-seq quantification with CellRanger"
shell = """
cellranger count --id={sample} \
                 --fastqs=raw/ \
                 --sample={sample} \
                 --transcriptome={config.reference} \
                 --localcores={threads} \
                 --localmem=60
"""

[rules.environment]
docker = "10xgenomics/cellranger:7.1.0"

[[rules]]
name = "clustering_analysis"
input = ["counts/{sample}/outs/filtered_feature_bc_matrix.h5"]
output = ["analysis/{sample}/seurat_object.rds", "analysis/{sample}/tsne_plot.png"]
threads = 4
memory = "16G"
description = "Cell clustering and visualization with Seurat"
shell = "Rscript scripts/seurat_analysis.R --input {input[0]} --output-dir analysis/{sample}/"

[rules.environment]
conda = "envs/seurat.yaml"

[[rules]]
name = "generate_sc_report"
input = ["analysis/{sample}/seurat_object.rds", "analysis/{sample}/tsne_plot.png"]
output = ["results/{sample}.sc_report.html"]
description = "Generate single-cell analysis report"
shell = "Rscript -e \"rmarkdown::render('templates/sc_report.Rmd', output_file='{output[0]}')\""

[rules.environment]
conda = "envs/rmarkdown.yaml"

Scientific Context#

Why Single-Cell?#

Traditional "bulk" RNA-seq measures the average expression across thousands of cells, masking biological heterogeneity. scRNA-seq reveals:

  • Cellular Heterogeneity — Identify rare cell types and sub-populations
  • Dynamic Processes — Trace cell differentiation and state transitions
  • Spatial Resolution — Map cell types back to tissue architecture

Computational Challenges#

scRNA-seq workflows are significantly more resource-intensive than bulk RNA-seq:

  • Memory Pressure — Alignment to large transcriptomes and UMI counting can require 64GB+ of RAM.
  • Sparse Data — Downstream analysis handles sparse matrices with millions of entries (cells × genes).
  • Environment Management — Often requires complex combinations of R (Seurat) and Python (Scanpy) tools.

Running the Workflow#

Validate#

$ oxo-flow validate examples/gallery/09_single_cell_rnaseq.oxoflow
✓ examples/gallery/09_single_cell_rnaseq.oxoflow — 3 rules, 2 dependencies

Further Reading#