Integrate scRNA-seq datasets#

import lamindb as ln
import lnschema_bionty as lb
import pandas as pd
import anndata as ad

ln.track()

Query files based on metadata#

ln.File.filter(tissues__name__icontains="lymph node").distinct().df()

ln.File.filter(cell_types__name__icontains="monocyte").distinct().df()

ln.File.filter(labels__name="female").distinct().df()

Intersect measured genes between two datasets#

file1 = ln.File.filter(description="Conde22").one()
file2 = ln.File.filter(description="10x reference pbmc68k").one()

file1.describe()

file1.view_lineage()

file2.describe()

file2.view_lineage()

file1_adata = file1.load()
file2_adata = file2.load()

file2_adata.obs.cell_type.head()

Here we compute shared genes without loading files:

file1_genes = file1.features["var"]
file2_genes = file2.features["var"]

shared_genes = file1_genes & file2_genes
shared_genes.list("symbol")[:10]

We also need to convert the ensembl_gene_id to symbol for file2 so that they can be concatenated:

mapper = (
    pd.DataFrame(file2_genes.values_list("ensembl_gene_id", "symbol"))
    .drop_duplicates(0)
    .set_index(0)[1]
)
mapper.head()

file1_adata.var.rename(index=mapper, inplace=True)

Intersect cell types#

file1_celltypes = file1.cell_types.all()
file2_celltypes = file2.cell_types.all()

shared_celltypes = file1_celltypes & file2_celltypes
shared_celltypes_names = shared_celltypes.list("name")
shared_celltypes_names

We can now subset the two datasets by shared cell types:

file1_adata_subset = file1_adata[
    file1_adata.obs["cell_type"].isin(shared_celltypes_names)
]
file1_adata_subset.obs["cell_type"].value_counts()

file2_adata_subset = file2_adata[
    file2_adata.obs["cell_type"].isin(shared_celltypes_names)
]
file2_adata_subset.obs["cell_type"].value_counts()

adata_concat = ad.concat(
    [file1_adata_subset, file2_adata_subset],
    label="file",
    keys=[file1.description, file2.description],
)
adata_concat

adata_concat.obs.value_counts()