scFlex

scFlex is an R package for converting single cell classes among Seurat, SingleCellExperiment, AnnData, and Loom.

The goal is not merely to produce a file with a new extension. scFlex checks cell/feature alignment, distinguishes raw counts from normalized expression, preserves compatible metadata and embeddings, and reports when a target format cannot represent part of the source object.

Overview

Supported conversions

Loom has a smaller and increasingly legacy data model. scFlex supports it as an interchange format but does not claim lossless preservation of components Loom cannot represent.

Installation

# install.packages("remotes")
remotes::install_github("mohamednhassan/scFlex")

Python setup

scFlex does not require a hard-coded Conda environment. It declares its Python requirements through reticulate::py_require() and lets reticulate resolve them in the user’s Python configuration.

For AnnData conversion, scFlex declares anndata>=0.10. Loom conversion additionally declares loompy>=3.0 only when Loom support is used.

Basic usage

1. Inspect the object first

Before conversion, inspect the input object to understand its structure and available components.

inspect_sc("object.rds")
inspect_sc("object.h5ad")
inspect_sc("object.loom")

inspect_sc() reports the detected object structure, including information such as assays/layers, dimensions, metadata, and dimensional reductions, without assigning a subjective conversion score.

2. Convert with convert_sc()

After inspection, use the general convert_sc() interface:

convert_sc(
  input = "object.rds",
  output = "object.h5ad",
  source = "seurat",
  destination = "anndata"
)

Another example:

convert_sc(
  input = "object.h5ad",
  output = "object_sce.rds",
  source = "anndata",
  destination = "sce"
)

Format-specific conversion functions are also available:

convert_seurat_to_anndata("object.rds", "object.h5ad")
convert_anndata_to_seurat("object.h5ad", "object.rds")

convert_seurat_to_sce("object.rds", "object_sce.rds")
convert_sce_to_seurat("object_sce.rds", "object.rds")

convert_sce_to_anndata("object_sce.rds", "object.h5ad")
convert_anndata_to_sce("object.h5ad", "object_sce.rds")

convert_anndata_to_loom("object.h5ad", "object.loom")
convert_loom_to_anndata("object.loom", "object.h5ad")

Seurat assay conversion

scFlex also provides helper functions for converting between classic Seurat Assay objects and Seurat v5 Assay5 objects.

Seurat v5 Assay5 to classic Assay

convert_seu_v5_to_classic(
  input = "object.rds",
  output = "object_classic.rds",
  assay = "RNA"
)

Classic Seurat Assay to Seurat v5 Assay5

convert_seu_classic_to_v5(
  input = "object.rds",
  output = "object_v5.rds",
  assay = "RNA"
)

These functions are useful when working with tools or workflows that expect a particular Seurat assay structure.

Preservation model

Typical mappings include:

Seurat v5 split layers

scFlex recognizes both canonical layers such as counts/data and split Seurat v5 layers such as:

counts.sample1
counts.sample2
data.sample1
data.sample2

Matching split layers are joined internally on a temporary assay for conversion; the input object is not modified.

Counts-only Seurat objects are also valid conversion inputs. When normalized expression is absent, AnnData X is populated with the raw counts without normalization, and the conversion result reports this explicitly.

Development

devtools::document()
devtools::test()
devtools::check()

Current scope

Version 0.1.0 focuses on expression matrices, cell/feature metadata, and dimensional reductions. Graphs, neighbors, Seurat command history, variable-feature state, feature loadings, multimodal altExp/MuData mapping, and spatial structures are not yet guaranteed to round-trip.

License

MIT.