Interoperability with `scirpy`

It is now possible to convert the file formats between dandelion>=0.1.1 and scirpy>=0.6.2 [Sturm2020] to enhance the collaboration between the analysis toolkits.

We will download the airr_rearrangement.tsv file from here:

# bash
wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_10k/sc5p_v2_hs_PBMC_10k_b_airr_rearrangement.tsv

Gene expression data can also be obtained here

# bash
wget https://cf.10xgenomics.com/samples/cell-vdj/4.0.0/sc5p_v2_hs_PBMC_10k/sc5p_v2_hs_PBMC_10k_filtered_feature_bc_matrix.h5

Import dandelion module

[1]:

# import sys
# sys.path.append("C://Users//Amos Choo//Desktop//dandelion")
import os

import dandelion as ddl

# change directory to somewhere more workable

# os.chdir(os.path.expanduser("~/Downloads/dandelion_tutorial/"))

ddl.logging.print_versions()

dandelion==0.3.4.dev74 pandas==2.0.1 numpy==1.24.3 matplotlib==3.7.1 networkx==3.1 scipy==1.11.2

[2]:

import scirpy as ir
import scanpy as sc


ir.__version__

[2]:

'0.14.0'

`dandelion`

[3]:

# read in the airr_rearrangement.tsv file
file_location = "sc5p_v2_hs_PBMC_10k_b_airr_rearrangement.tsv"

# read in gene expression data
adata = sc.read_10x_h5("sc5p_v2_hs_PBMC_10k_filtered_feature_bc_matrix.h5")
adata.var_names_make_unique()

vdj = ddl.read_10x_airr(file_location)
vdj

/opt/homebrew/Caskroom/mambaforge/base/envs/dandelion/lib/python3.11/site-packages/anndata/_core/anndata.py:1832: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.

[3]:

Dandelion class object with n_obs = 994 and n_contigs = 2093
    data: 'cell_id', 'sequence_id', 'sequence', 'sequence_aa', 'productive', 'rev_comp', 'v_call', 'v_cigar', 'd_call', 'd_cigar', 'j_call', 'j_cigar', 'c_call', 'c_cigar', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'junction_length', 'junction_aa_length', 'v_sequence_start', 'v_sequence_end', 'd_sequence_start', 'd_sequence_end', 'j_sequence_start', 'j_sequence_end', 'c_sequence_start', 'c_sequence_end', 'consensus_count', 'duplicate_count', 'is_cell', 'locus', 'rearrangement_status'
    metadata: 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'c_call_B_VDJ', 'c_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'v_call_VDJ_main', 'v_call_VJ_main', 'd_call_VDJ_main', 'j_call_VDJ_main', 'j_call_VJ_main', 'c_call_VDJ_main', 'c_call_VJ_main', 'v_call_B_VDJ_main', 'd_call_B_VDJ_main', 'j_call_B_VDJ_main', 'v_call_B_VJ_main', 'j_call_B_VJ_main', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

The test file contains a blank clone_id column so we run find_clones to populate it first.

[4]:

ddl.tl.find_clones(vdj)

Finding clones based on B cell VDJ chains : 100%|██████████| 157/157 [00:00<00:00, 7623.45it/s]
Finding clones based on B cell VJ chains : 100%|██████████| 164/164 [00:00<00:00, 10267.42it/s]
Refining clone assignment based on VJ chain pairing : 100%|██████████| 994/994 [00:00<00:00, 586129.37it/s]

`ddl.to_scirpy` : Converting `dandelion` to `scirpy`

[5]:

irdata = ddl.to_scirpy(vdj)
irdata

/opt/homebrew/Caskroom/mambaforge/base/envs/dandelion/lib/python3.11/site-packages/anndata/_core/aligned_mapping.py:54: ExperimentalFeatureWarning: Support for Awkward Arrays is currently experimental. Behavior may change in the future. Please report any issues you may encounter!

[5]:

MuData object with n_obs × n_vars = 994 × 0
  1 modality
    airr:   994 x 0
      obsm: 'airr'

Conversion to AnndData in scirpy format is also available

[6]:

mudata = ddl.to_scirpy(vdj, to_mudata=False)
mudata

[6]:

AnnData object with n_obs × n_vars = 994 × 0
    obsm: 'airr'

If you have gene expression data, the parameter gex_adata supports the gene expression data in AnnData format.

Please note that this will slice to the same cell_id that are present in the same in the AIRR data.

[7]:

irdata = ddl.to_scirpy(vdj, to_mudata=False, gex_adata=adata)
irdata

/opt/homebrew/Caskroom/mambaforge/base/envs/dandelion/lib/python3.11/site-packages/anndata/_core/anndata.py:117: ImplicitModificationWarning: Transforming to str index.

[7]:

AnnData object with n_obs × n_vars = 984 × 36601
    var: 'gene_ids', 'feature_types', 'genome'
    obsm: 'airr'

[8]:

mudata = ddl.to_scirpy(vdj, to_mudata=True, gex_adata=adata)
mudata

[8]:

MuData object with n_obs × n_vars = 10563 × 36601
  2 modalities
    gex:    10553 x 36601
      var:  'gene_ids', 'feature_types', 'genome'
    airr:   994 x 0
      obsm: 'airr'

Use scirpy’s get functions to retrieve the relevant airr info (https://scirpy.scverse.org/en/latest/generated/scirpy.get.airr.html)

[9]:

ir.get.airr(irdata, "clone_id")

WARNING: No chain indices found under adata.obsm['chain_indices']. Running scirpy.pp.index_chains with default parameters.

[9]:

	VJ_1_clone_id	VDJ_1_clone_id	VJ_2_clone_id	VDJ_2_clone_id
AAACCTGTCATATCGG-1	B_VJ_98_1_2	None	None	None
AAACCTGTCCGTTGTC-1	B_VDJ_113_3_1_VJ_152_2_1	B_VDJ_113_3_1_VJ_152_2_1	None	None
AAACCTGTCGAGAACG-1	B_VDJ_107_4_1_VJ_162_1_1	B_VDJ_107_4_1_VJ_162_1_1	None	None
AAACCTGTCTTGAGAC-1	B_VDJ_157_1_1_VJ_4_1_1	B_VDJ_157_1_1_VJ_4_1_1	None	None
AAACGGGAGCGACGTA-1	B_VDJ_98_1_2_VJ_63_2_3	B_VDJ_98_1_2_VJ_63_2_3	None	None
...	...	...	...	...
TTTGCGCTCTAACGGT-1	B_VDJ_88_3_1_VJ_122_1_2	B_VDJ_88_3_1_VJ_122_1_2	None	None
TTTGGTTGTAGCCTAT-1	B_VDJ_40_1_1_VJ_112_2_1	B_VDJ_40_1_1_VJ_112_2_1	None	None
TTTGGTTTCAGAGCTT-1	B_VDJ_102_5_2_VJ_153_1_1	B_VDJ_102_5_2_VJ_153_1_1	None	None
TTTGGTTTCAGTGTTG-1	B_VDJ_114_1_1_VJ_1_2_1	B_VDJ_114_1_1_VJ_1_2_1	None	None
TTTGGTTTCGGTGTCG-1	B_VDJ_131_1_1_VJ_158_3_1	B_VDJ_131_1_1_VJ_158_3_1	None	None

984 rows × 4 columns

[10]:

ir.get.airr(mudata, "clone_id")

WARNING: No chain indices found under adata.obsm['chain_indices']. Running scirpy.pp.index_chains with default parameters.

[10]:

	VJ_1_clone_id	VDJ_1_clone_id	VJ_2_clone_id	VDJ_2_clone_id
cell_id
AAACCTGTCATATCGG-1	B_VJ_98_1_2	None	None	None
AAACCTGTCCGTTGTC-1	B_VDJ_113_3_1_VJ_152_2_1	B_VDJ_113_3_1_VJ_152_2_1	None	None
AAACCTGTCGAGAACG-1	B_VDJ_107_4_1_VJ_162_1_1	B_VDJ_107_4_1_VJ_162_1_1	None	None
AAACCTGTCTTGAGAC-1	B_VDJ_157_1_1_VJ_4_1_1	B_VDJ_157_1_1_VJ_4_1_1	None	None
AAACGGGAGCGACGTA-1	B_VDJ_98_1_2_VJ_63_2_3	B_VDJ_98_1_2_VJ_63_2_3	None	None
...	...	...	...	...
TTTGCGCTCTAACGGT-1	B_VDJ_88_3_1_VJ_122_1_2	B_VDJ_88_3_1_VJ_122_1_2	None	None
TTTGGTTGTAGCCTAT-1	B_VDJ_40_1_1_VJ_112_2_1	B_VDJ_40_1_1_VJ_112_2_1	None	None
TTTGGTTTCAGAGCTT-1	B_VDJ_102_5_2_VJ_153_1_1	B_VDJ_102_5_2_VJ_153_1_1	None	None
TTTGGTTTCAGTGTTG-1	B_VDJ_114_1_1_VJ_1_2_1	B_VDJ_114_1_1_VJ_1_2_1	None	None
TTTGGTTTCGGTGTCG-1	B_VDJ_131_1_1_VJ_158_3_1	B_VDJ_131_1_1_VJ_158_3_1	None	None

994 rows × 4 columns

Or you can add transfer = True, which will perform dandelion’s tl.transfer.

[11]:

irdatax = ddl.to_scirpy(vdj, transfer=True)
irdatax

[11]:

MuData object with n_obs × n_vars = 994 × 0
  1 modality
    airr:   994 x 0
      obs:  'clone_id', 'clone_id_by_size', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'c_call_B_VDJ', 'c_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'v_call_VDJ_main', 'v_call_VJ_main', 'd_call_VDJ_main', 'j_call_VDJ_main', 'j_call_VJ_main', 'c_call_VDJ_main', 'c_call_VJ_main', 'v_call_B_VDJ_main', 'd_call_B_VDJ_main', 'j_call_B_VDJ_main', 'v_call_B_VJ_main', 'j_call_B_VJ_main', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'
      obsm: 'airr'

[12]:

irdatax = ddl.to_scirpy(vdj, transfer=True, to_mudata=False)
irdatax

[12]:

AnnData object with n_obs × n_vars = 994 × 0
    obs: 'clone_id', 'clone_id_by_size', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'c_call_B_VDJ', 'c_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'v_call_VDJ_main', 'v_call_VJ_main', 'd_call_VDJ_main', 'j_call_VDJ_main', 'j_call_VJ_main', 'c_call_VDJ_main', 'c_call_VJ_main', 'v_call_B_VDJ_main', 'd_call_B_VDJ_main', 'j_call_B_VDJ_main', 'v_call_B_VJ_main', 'j_call_B_VJ_main', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'
    obsm: 'airr'

`ddl.from_scirpy` : Converting `scirpy` to `dandelion`

Converting MuData back to Dandelion

[13]:

vdjx = ddl.from_scirpy(mudata)
vdjx

[13]:

Dandelion class object with n_obs = 994 and n_contigs = 2093
    data: 'c_call', 'c_cigar', 'c_sequence_end', 'c_sequence_start', 'clone_id', 'consensus_count', 'd_call', 'd_cigar', 'd_sequence_end', 'd_sequence_start', 'duplicate_count', 'germline_alignment', 'is_cell', 'j_call', 'j_cigar', 'j_sequence_end', 'j_sequence_start', 'junction', 'junction_aa', 'junction_aa_length', 'junction_length', 'locus', 'productive', 'rearrangement_status', 'rev_comp', 'sequence', 'sequence_aa', 'sequence_alignment', 'sequence_id', 'v_call', 'v_cigar', 'v_sequence_end', 'v_sequence_start', 'cell_id'
    metadata: 'clone_id', 'clone_id_by_size', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'c_call_B_VDJ', 'c_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'v_call_VDJ_main', 'v_call_VJ_main', 'd_call_VDJ_main', 'j_call_VDJ_main', 'j_call_VJ_main', 'c_call_VDJ_main', 'c_call_VJ_main', 'v_call_B_VDJ_main', 'd_call_B_VDJ_main', 'j_call_B_VDJ_main', 'v_call_B_VJ_main', 'j_call_B_VJ_main', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

Converting AnnData back to Dandelion

[14]:

vdjx = ddl.from_scirpy(irdata)
vdjx

[14]:

Dandelion class object with n_obs = 984 and n_contigs = 2073
    data: 'c_call', 'c_cigar', 'c_sequence_end', 'c_sequence_start', 'clone_id', 'consensus_count', 'd_call', 'd_cigar', 'd_sequence_end', 'd_sequence_start', 'duplicate_count', 'germline_alignment', 'is_cell', 'j_call', 'j_cigar', 'j_sequence_end', 'j_sequence_start', 'junction', 'junction_aa', 'junction_aa_length', 'junction_length', 'locus', 'productive', 'rearrangement_status', 'rev_comp', 'sequence', 'sequence_aa', 'sequence_alignment', 'sequence_id', 'v_call', 'v_cigar', 'v_sequence_end', 'v_sequence_start', 'cell_id'
    metadata: 'clone_id', 'clone_id_by_size', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_B_VJ', 'j_call_B_VJ', 'c_call_B_VDJ', 'c_call_B_VJ', 'productive_B_VDJ', 'productive_B_VJ', 'duplicate_count_B_VDJ', 'duplicate_count_B_VJ', 'v_call_VDJ_main', 'v_call_VJ_main', 'd_call_VDJ_main', 'j_call_VDJ_main', 'j_call_VJ_main', 'c_call_VDJ_main', 'c_call_VJ_main', 'v_call_B_VDJ_main', 'd_call_B_VDJ_main', 'j_call_B_VDJ_main', 'v_call_B_VJ_main', 'j_call_B_VJ_main', 'isotype', 'isotype_status', 'locus_status', 'chain_status', 'rearrangement_status_VDJ', 'rearrangement_status_VJ'

[15]:

vdjx.metadata

[15]:

	clone_id	clone_id_by_size	locus_VDJ	locus_VJ	productive_VDJ	productive_VJ	v_call_VDJ	d_call_VDJ	j_call_VDJ	v_call_VJ	...	d_call_B_VDJ_main	j_call_B_VDJ_main	v_call_B_VJ_main	j_call_B_VJ_main	isotype	isotype_status	locus_status	chain_status	rearrangement_status_VDJ	rearrangement_status_VJ
AAACCTGTCATATCGG-1	B_VJ_98_1_2	6	None	IGK	None	True	None	None	None	IGKV1-8	...	None	None	IGKV1-8	IGKJ4	None	None	Orphan IGK	Orphan VJ	None	standard
AAACCTGTCCGTTGTC-1	B_VDJ_113_3_1_VJ_152_2_1	957	IGH	IGK	True	True	IGHV1-69D	IGHD3-22	IGHJ3	IGKV1-8	...	IGHD3-22	IGHJ3	IGKV1-8	IGKJ1	IgM	IgM	IGH + IGK	Single pair	standard	standard
AAACCTGTCGAGAACG-1	B_VDJ_107_4_1_VJ_162_1_1	773	IGH	IGL	True	True	IGHV1-2	nan	IGHJ3	IGLV5-45	...	nan	IGHJ3	IGLV5-45	IGLJ3	IgM	IgM	IGH + IGL	Single pair	standard	standard
AAACCTGTCTTGAGAC-1	B_VDJ_157_1_1_VJ_4_1_1	772	IGH	IGK	True	True	IGHV5-51	nan	IGHJ3	IGKV1D-8	...	nan	IGHJ3	IGKV1D-8	IGKJ2	IgM	IgM	IGH + IGK	Single pair	standard	standard
AAACGGGAGCGACGTA-1	B_VDJ_98_1_2_VJ_63_2_3	771	IGH	IGL	True	True	IGHV4-59	nan	IGHJ3	IGLV3-19	...	nan	IGHJ3	IGLV3-19	IGLJ2	IgM	IgM	IGH + IGL	Single pair	standard	standard
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
TTTGCGCTCTAACGGT-1	B_VDJ_88_3_1_VJ_122_1_2	386	IGH	IGL	True	True	IGHV3-43	nan	IGHJ6	IGLV2-8	...	nan	IGHJ6	IGLV2-8	IGLJ3	IgM	IgM	IGH + IGL	Single pair	standard	standard
TTTGGTTGTAGCCTAT-1	B_VDJ_40_1_1_VJ_112_2_1	385	IGH	IGK	True	True	IGHV4-39	nan	IGHJ2	IGKV6-21	...	nan	IGHJ2	IGKV6-21	IGKJ4	IgM	IgM	IGH + IGK	Single pair	standard	standard
TTTGGTTTCAGAGCTT-1	B_VDJ_102_5_2_VJ_153_1_1	384	IGH	IGK	True	True	IGHV7-4-1	IGHD3-10	IGHJ4	IGKV3-11	...	IGHD3-10	IGHJ4	IGKV3-11	IGKJ5	IgM	IgM	IGH + IGK	Single pair	standard	standard
TTTGGTTTCAGTGTTG-1	B_VDJ_114_1_1_VJ_1_2_1	383	IGH	IGL	True	True	IGHV2-5	nan	IGHJ4	IGLV2-23	...	nan	IGHJ4	IGLV2-23	IGLJ2	IgM	IgM	IGH + IGL	Single pair	standard	standard
TTTGGTTTCGGTGTCG-1	B_VDJ_131_1_1_VJ_158_3_1	1149	IGH	IGK	True	True	IGHV3-21	nan	IGHJ2	IGKV3-11	...	nan	IGHJ2	IGKV3-11	IGKJ4	IgM	IgM	IGH + IGK	Single pair	standard	standard

984 rows × 46 columns

This time, find clones with scirpy’s method.

[16]:

ir.tl.chain_qc(irdata)
ir.pp.ir_dist(irdata, metric="hamming", sequence="aa")
ir.tl.define_clonotypes(irdata)
irdata

[16]:

AnnData object with n_obs × n_vars = 984 × 36601
    obs: 'receptor_type', 'receptor_subtype', 'chain_pairing', 'clone_id', 'clone_id_size'
    var: 'gene_ids', 'feature_types', 'genome'
    uns: 'chain_indices', 'ir_dist_aa_hamming', 'ir_dist_nt_identity', 'clone_id'
    obsm: 'airr', 'chain_indices'

Visualising with `scirpy`’s plotting tools

You can now also plot dandelion networks using scirpy’s functions.

[17]:

ddl.tl.generate_network(vdj, key="junction")

Setting up data: 2093it [00:00, 21322.38it/s]
Calculating distances : 100%|██████████| 1154/1154 [00:00<00:00, 11734.08it/s]
Aggregating distances : 100%|██████████| 4/4 [00:00<00:00, 381.53it/s]
Sorting into clusters : 100%|██████████| 1154/1154 [00:00<00:00, 4742.88it/s]
Calculating minimum spanning tree : 100%|██████████| 8/8 [00:00<00:00, 1173.89it/s]
Generating edge list : 100%|██████████| 8/8 [00:00<00:00, 4087.52it/s]
Computing overlap : 100%|██████████| 1154/1154 [00:00<00:00, 3972.95it/s]
Adjust overlap : 100%|██████████| 82/82 [00:00<00:00, 3896.20it/s]
Linking edges : 100%|██████████| 984/984 [00:00<00:00, 93606.29it/s]

Computing network layout
Computing expanded network layout

[18]:

irdata.obs["scirpy_clone_id"] = irdata.obs["clone_id"]  # stash it
ddl.tl.transfer(
    irdata, vdj, overwrite=True
)  # overwrite scirpy's clone_id definition

[19]:

ir.tl.clonotype_network(irdata, min_cells=2)
ir.pl.clonotype_network(irdata, color="clone_id", panel_size=(7, 7))

[19]:

<Axes: >

../_images/notebooks_1c_dandelion_scirpy_32_1.png

to swap to a shorter clone_id name (ordered by size)

[20]:

ddl.tl.transfer(irdata, vdj, clone_key="clone_id_by_size")
ir.tl.clonotype_network(irdata, clonotype_key="clone_id_by_size", min_cells=2)
ir.pl.clonotype_network(irdata, color="clone_id_by_size", panel_size=(7, 7))

[20]:

<Axes: >

../_images/notebooks_1c_dandelion_scirpy_34_1.png

you can also collapse the networks to a single node and plot by size

[21]:

ddl.tl.transfer(irdata, vdj, clone_key="clone_id_by_size", collapse_nodes=True)
ir.tl.clonotype_network(irdata, clonotype_key="clone_id_by_size", min_cells=2)
ir.pl.clonotype_network(irdata, color="scirpy_clone_id", panel_size=(7, 7))

[21]:

<Axes: >

../_images/notebooks_1c_dandelion_scirpy_36_1.png

[ ]:

Interoperability with scirpy

dandelion

ddl.to_scirpy : Converting dandelion to scirpy

ddl.from_scirpy : Converting scirpy to dandelion

Visualising with scirpy’s plotting tools

Interoperability with `scirpy`

`dandelion`

`ddl.to_scirpy` : Converting `dandelion` to `scirpy`

`ddl.from_scirpy` : Converting `scirpy` to `dandelion`

Visualising with `scirpy`’s plotting tools