Seurat object (RDS)

write.csv() on a full assay tries to densify a matrix that's sparse for a reason, export only what you need or you'll watch R run out of memory on a 40k-cell object.

Seurat v5's split layers and multiple assays don't collapse into a single AnnData X on their own; pick the assay and layer yourself or the converter will guess wrong.

SaveH5Seurat doesn't corrupt your data on Seurat v5 objects, it just refuses to write until you rejoin split layers, and knowing why saves you an hour of guessing.

SaveLoom writes one assay and can quietly hand you scaled data instead of raw counts, so check the matrix before you trust it in pySCENIC or velocyto.

Pick the wrong Seurat layer, or reach for Matrix::writeMM instead of write10xCounts, and your MTX directory looks complete right up until Scanpy or Cell Ranger tries to load it.

as.SingleCellExperiment() is one line, but that line decides which assay becomes primary, drops your scaled matrix, and can hand back an empty counts assay without ever throwing an error.

The matrix loads without error every time; whether your gene IDs survive the trip is a separate question you have to check yourself.

Read10X_h5 hands you a matrix with the wrong row names by default, and nobody notices until a marker gene lookup comes back empty.

anndataR and capseuratconverter handle the transpose and slot mapping for you; deciding which layer actually becomes your counts slot is still on you.

SeuratDisk's Connect-then-as.Seurat path works, but velocyto barcodes and Seurat v5 layers will quietly wreck your merge if you skip the cleanup step.

Read10X reads a directory, not a file, and the column you pick for gene names quietly decides how many genes you actually have.

as.Seurat() copies your matrices and cell metadata fine; it quietly leaves your gene annotations and altExps behind.