Comments (2)
Hey @christiankroy, Just curious... were you able to solve this issue? I am running the same issue right now when trying to convert an existing seurat object to loom.
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Hi @kathirij - I did figure out a work around. I ended up converting to anndata, importing into python, and writing a loom file that way. Here's some example code.
First Seurat::Convert()
the Seurat object (so
) to anndata format and write a .h5ad
file to disk.
NB - you'll want to add as much as possible to the [email protected]
slot (dimension reduction coordinates/per cell values/ etc) before exporting because it comes along with the anndata
export and is automatically included in the loom file. Otherwise you'll have to add it within the python chunk.
Seurat::Convert(
from = so,
to = "anndata",
filename = "./Seurat_analysis.h5ad",
overwrite = TRUE,
display.progress = TRUE)
Then load the reticulate
Rstudio package (I work in Rstudio).
library(reticulate)
Then load a python3 conda environment (I called mine loom
below) with scanpy
and loompy
installed.
reticulate::use_condaenv("loom")
And then within a reticulate/python
code block.
import numpy as np
import pandas as pd
import scanpy.api as sc
import scipy.sparse as sparse
import loompy as lp
adata = sc.read_h5ad("./Seurat_analysis.h5ad")
adata.write_loom("./Seurat-based.loom")
ds = lp.connect("./Seurat-based.loom")
ds.ra.GENENAME = list(adata.var_names) # Adds GENENAMES instead of index locations
And you're done.
I've been able to use loom-viewer from the Linnarsson lab to interact with these loom
files. Works great and major hat-tip to Linnarsson lab.
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