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Comment by tetris11 | Hacker News Reader

oh wow, I made a sweeping generalization - apologies. In single cell, bulk RNA-seq, ATAC, ChIP, I'd say that there are more R packages for the analysis of these omics than Python packages.

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Python is catching up on single-cell transcriptomics; see AnnData, Scanpy, et al.

ScanPy/AnnData has been dead in the water for a while now, and most people use Seurat due to its operability with many many downstream extensions

> has been dead in the water for a while now

Both are under active development and are used in several transcriptomics atlas projects, as far as I can tell.

Those atlases were established back when Scanpy and Seurat were relatively beta and were still fighting out the tool space.

Look at the packages now for integration, pseudo time, pseudobulk - R (and therefore Seurat) dominates heavily

Disagree - Seurat had first mover advantage with single cell but sucked with larger datasets that Scanpy could handle till the big change in Seurat 5. The preference for either Seurat/Scanpy is incredibly lab specific. That said, Seurat is better documented for sure, but the ecosystem for both is incredibly rich and flourishing.

yeah, I also disagree with this. It's true Seurat is still heavily used for scRNA/scATAC but I see most new models increasingly being written/tooled for python and based on anndata. Geneformer, scGPT, scVI etc. I wish there was better operability between the scverse stuff and Seurat, but Seurat went their own way from SCE/bioconductor so that's probably not going to happen.

To be fair to them, getting anything submitted to buoconductor requires a ton of effort, and the pay off is often less concise code

I have no dog in the Scanpy/Seurat argument, but AnnData is becoming very popular as a data format even outside of single-cell omics.

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