Running SingleR from Python

singler.py

import scanpy as sc

# numpy et al.
import numpy as np
import scipy.sparse as sp
import pandas as pd
import gc

# R integration
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri, numpy2ri, r
from rpy2.robjects.vectors import StrVector, FloatVector, ListVector
import rpy2.robjects as ro
import anndata2ri

def predict_cell_types(adata,
                       use_raw=True,
                       species='mouse',
                       ref_name=None,
                       ref_label_column='label.fine',
                       cluster_key='leiden',
                       obs_out_key='predicted_cell_types',
                       uns_out_key='cell_type_prediction',
                       **kwds):

    r('BiocParallel::register(BiocParallel::SerialParam())')
    s = importr('SingleR')

    if ref_name is None:
        if species == 'mouse':
            ref_names = ['MouseRNAseqData', 'ImmGenData']
        else:
            ref_names = ['HumanPrimaryCellAtlasData', 'MonacoImmuneData']

        refs = [s.__dict__[ref_name]() for ref_name in ref_names]
        ref_genes = StrVector(set.intersection(*[set(r['rownames'](d)) for d in refs]))
        ref = r('cbind')(*[r('`[`')(d, ref_genes) for d in refs]) # merge references
    else:
        if isinstance(ref_name, str):
            ref = r(f'scRNAseq::{ref_name}')()
            ref = r('scater::logNormCounts')(ref)
        else:
            ref = ref_name
        ref_genes = r['rownames'](ref)

    ad = adata.raw if use_raw else adata
    obs = adata.obs

    common_genes = sorted(list(set(ad.var_names) & set(ref_genes)))
    assert len(common_genes) > 1000, 'Not enough genes overlapping with ref SingleR datasets...'

    ref = r('`[`')(ref, ro.vectors.StrVector(common_genes))

    mat = ad[:, common_genes].X.T.copy()

    if sp.issparse(mat):
        mat = anndata2ri.scipy2ri.py2rpy(sp.csr_matrix(mat))
    else:
        mat = numpy2ri.py2rpy(mat)

    mat = r("`rownames<-`")(mat, ro.vectors.StrVector(ad[:, common_genes].var_names))
    mat = r("`colnames<-`")(mat, ro.vectors.StrVector(obs.index)) # TODO: really needed?
    clusters = ro.vectors.StrVector(obs[cluster_key].values.tolist())

    par = r('BiocParallel::SerialParam')()
    labels = s.SingleR(test=mat,
                   ref=ref,
                   labels=r('`$`')(ref, ref_label_column), # use label.main too
                   method='cluster',
                   clusters=clusters, BPPARAM=par, **kwds)

    labels = pandas2ri.rpy2py(r('as.data.frame')(labels))

    adata.obs[obs_out_key] = ''
    for cluster in adata.obs[cluster_key].cat.categories:
        adata.obs.loc[adata.obs[cluster_key] == cluster, obs_out_key] = labels.loc[cluster]['pruned.labels']

    adata.uns[uns_out_key] = labels['pruned.labels'].to_dict()

I think you need instal R packeage 'BiocParallel' first