R vs Python for Single-Cell Analysis: My Honest Take

After using both extensively, here's when I choose R (Seurat) vs Python (Scanpy)

The eternal debate. After years of using both, here’s my honest assessment.

When I Choose Python (Scanpy)

→ Large datasets — Better memory management

→ Pipeline integration — Snakemake, Nextflow

→ Deep learning — PyTorch, TensorFlow integration

→ Reproducibility — Conda environments

When I Choose R (Seurat)

→ Quick exploration — RStudio’s interactivity

→ Publication figures — ggplot2 is unmatched

→ Statistical analysis — R’s statistical heritage

→ Collaborator preference — Many biologists know R

The Honest Truth

I use both. My typical workflow:

1. Preprocessing — Python (faster, scalable)
2. Exploration — R (better visualization)
3. Final figures — R (ggplot2 + patchwork)
4. Production pipeline — Python (automation)

The Interoperability Solution

# Save from Scanpy for Seurat
adata.write('data.h5ad')

# Load in Seurat
library(SeuratDisk)
Convert("data.h5ad", dest = "h5seurat")
data <- LoadH5Seurat("data.h5seurat")

My Recommendation

→ Learn both — You’ll need both eventually

→ Start with Python — More transferable skills

→ Master ggplot2 — For publications

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Don’t pick sides. Pick the right tool for the job. What’s your preference? Let me know in the comments!

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