Calculating diversity and mutation

Calculating mutational load

To calculate mutational load, the functions from immcantation suite’s shazam [Gupta2015] can be accessed via rpy2 to work with the dandelion class object.

This can be run immediately after pp.reassign_alleles during the reannotation pre-processing stage because the required germline columns should be present in the genotyped .tsv file. I would recommend to run this after TIgGER [Gadala-Maria2015], after the v_calls were corrected. Otherwise, if the reannotation was skipped, you can run it now as follows:

Import modules

import os
import pandas as pd
import dandelion as ddl

ddl.logging.print_header()

dandelion==0.3.4.dev29 pandas==2.0.1 numpy==1.24.3 matplotlib==3.7.1 networkx==3.1 scipy==1.11.2

# change directory to somewhere more workable
os.chdir(os.path.expanduser("~/Downloads/dandelion_tutorial/"))
# I'm importing scanpy here to make use of its logging module.
import scanpy as sc

sc.settings.verbosity = 3
import warnings

warnings.filterwarnings("ignore")
sc.logging.print_header()

scanpy==1.9.3 anndata==0.9.1 umap==0.5.3 numpy==1.24.3 scipy==1.11.2 pandas==2.0.1 scikit-learn==1.3.0 statsmodels==0.14.0 python-igraph==0.10.6 pynndescent==0.5.10

Read in the previously saved files

adata = sc.read_h5ad("adata.h5ad")
adata

AnnData object with n_obs × n_vars = 22985 × 1464
    obs: 'sampleid', 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'gmm_pct_count_clusters_keep', 'scrublet_score', 'is_doublet', 'filter_rna', 'has_contig', 'sample_id', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_genotyped_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_genotyped_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_genotyped_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_genotyped_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', 'leiden', 'clone_id', 'clone_id_by_size', 'changeo_clone_id'
    var: 'feature_types', 'genome', 'gene_ids-0', 'gene_ids-1', 'gene_ids-2', 'gene_ids-3', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'chain_status_colors', 'clone_id', 'hvg', 'leiden', 'leiden_colors', 'locus_status_colors', 'log1p', 'neighbors', 'pca', 'rna_neighbors', 'sampleid_colors', 'umap'
    obsm: 'X_pca', 'X_umap', 'X_vdj'
    varm: 'PCs'
    obsp: 'connectivities', 'distances', 'rna_connectivities', 'rna_distances', 'vdj_connectivities', 'vdj_distances'

vdj = ddl.read_h5ddl("dandelion_results.h5ddl")
vdj

Dandelion class object with n_obs = 2071 and n_contigs = 4882
    data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'c_call', 'junction_length', 'np1_length', 'np2_length', 'v_sequence_start', 'v_sequence_end', 'v_germline_start', 'v_germline_end', 'd_sequence_start', 'd_sequence_end', 'd_germline_start', 'd_germline_end', 'j_sequence_start', 'j_sequence_end', 'j_germline_start', 'j_germline_end', 'v_score', 'v_identity', 'v_support', 'd_score', 'd_identity', 'd_support', 'j_score', 'j_identity', 'j_support', 'fwr1', 'fwr2', 'fwr3', 'fwr4', 'cdr1', 'cdr2', 'cdr3', 'cell_id', 'consensus_count', 'duplicate_count', 'v_call_10x', 'd_call_10x', 'j_call_10x', 'junction_10x', 'junction_10x_aa', 'j_support_igblastn', 'j_score_igblastn', 'j_call_igblastn', 'j_call_blastn', 'j_identity_blastn', 'j_alignment_length_blastn', 'j_number_of_mismatches_blastn', 'j_number_of_gap_openings_blastn', 'j_sequence_start_blastn', 'j_sequence_end_blastn', 'j_germline_start_blastn', 'j_germline_end_blastn', 'j_support_blastn', 'j_score_blastn', 'j_sequence_alignment_blastn', 'j_germline_alignment_blastn', 'j_source', 'd_support_igblastn', 'd_score_igblastn', 'd_call_igblastn', 'd_call_blastn', 'd_identity_blastn', 'd_alignment_length_blastn', 'd_number_of_mismatches_blastn', 'd_number_of_gap_openings_blastn', 'd_sequence_start_blastn', 'd_sequence_end_blastn', 'd_germline_start_blastn', 'd_germline_end_blastn', 'd_support_blastn', 'd_score_blastn', 'd_sequence_alignment_blastn', 'd_germline_alignment_blastn', 'd_source', 'v_call_genotyped', 'germline_alignment_d_mask', 'sample_id', 'c_sequence_alignment', 'c_germline_alignment', 'c_sequence_start', 'c_sequence_end', 'c_score', 'c_identity', 'c_call_10x', 'junction_aa_length', 'fwr1_aa', 'fwr2_aa', 'fwr3_aa', 'fwr4_aa', 'cdr1_aa', 'cdr2_aa', 'cdr3_aa', 'sequence_alignment_aa', 'v_sequence_alignment_aa', 'd_sequence_alignment_aa', 'j_sequence_alignment_aa', 'complete_vdj', 'j_call_multimappers', 'j_call_multiplicity', 'j_call_sequence_start_multimappers', 'j_call_sequence_end_multimappers', 'j_call_support_multimappers', 'mu_count', 'ambiguous', 'rearrangement_status', 'clone_id', 'changeo_clone_id'
    metadata: 'clone_id', 'clone_id_by_size', 'sample_id', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_genotyped_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_genotyped_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_genotyped_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_genotyped_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', 'changeo_clone_id'
    layout: layout for 2071 vertices, layout for 70 vertices
    graph: networkx graph of 2071 vertices, networkx graph of 70 vertices

# let's recreate the vdj object with only the first two samples
subset_data = vdj.data[
    vdj.data["sample_id"].isin(["sc5p_v2_hs_PBMC_1k", "sc5p_v2_hs_PBMC_10k"])
]
subset_data

	sequence_id	sequence	rev_comp	productive	v_call	d_call	j_call	sequence_alignment	germline_alignment	junction	...	j_call_multimappers	j_call_multiplicity	j_call_sequence_start_multimappers	j_call_sequence_end_multimappers	j_call_support_multimappers	mu_count	ambiguous	rearrangement_status	clone_id	changeo_clone_id
sequence_id
sc5p_v2_hs_PBMC_10k_AAACCTGTCCGTTGTC_contig_2	sc5p_v2_hs_PBMC_10k_AAACCTGTCCGTTGTC_contig_2	ATCACATAACAACCACATTCCTCCTCTAAAGAAGCCCCTGGGAGCA...	F	T	IGHV1-69*01,IGHV1-69D*01	IGHD3-22*01	IGHJ3*02	CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...	CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...	TGTGCGACTACGTATTACTATGATAGTAGTGGTTATTACCAGAATG...	...	IGHJ3*02	1.0	445.0	494.0	0.0	0	F	standard	B_VDJ_119_3_2_VJ_80_2_3	11_0
sc5p_v2_hs_PBMC_10k_AAACCTGTCCGTTGTC_contig_1	sc5p_v2_hs_PBMC_10k_AAACCTGTCCGTTGTC_contig_1	AGGAGTCAGACCCTGTCAGGACACAGCATAGACATGAGGGTCCCCG...	F	T	IGKV1-8*01		IGKJ1*01	GCCATCCGGATGACCCAGTCTCCATCCTCATTCTCTGCATCTACAG...	GCCATCCGGATGACCCAGTCTCCATCCTCATTCTCTGCATCTACAG...	TGTCAACAGTATTATAGTTACCCTCGGACGTTC	...	IGKJ1*01	1.0	380.0	415.0	0.0	0	F	standard	B_VDJ_119_3_2_VJ_80_2_3	11_0
sc5p_v2_hs_PBMC_10k_AAACCTGTCGAGAACG_contig_1	sc5p_v2_hs_PBMC_10k_AAACCTGTCGAGAACG_contig_1	ACTGTGGGGGTAAGAGGTTGTGTCCACCATGGCCTGGACTCCTCTC...	F	T	IGLV5-45*02		IGLJ3*02	CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...	CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...	TGTATGATTTGGCACAGCAGCGCTTGGGTGGTC	...	IGLJ3*01	1.0	402.0	431.0	0.0	8	F	standard	B_VDJ_42_1_2_VJ_54_1_1	150_1
sc5p_v2_hs_PBMC_10k_AAACCTGTCGAGAACG_contig_2	sc5p_v2_hs_PBMC_10k_AAACCTGTCGAGAACG_contig_2	GGGAGCATCACCCAGCAACCACATCTGTCCTCTAGAGAATCCCCTG...	F	T	IGHV1-2*02		IGHJ3*02	CAGGTGCAACTGGTGCAGTCTGGGGGT...GAGGTAAAGAAGCCTG...	CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...	TGTGCGAGAGAGATAGAGGGGGACGGTGTTTTTGAAATCTGG	...	IGHJ3*02	1.0	433.0	479.0	0.0	22	F	standard	B_VDJ_42_1_2_VJ_54_1_1	150_1
sc5p_v2_hs_PBMC_10k_AAACCTGTCTTGAGAC_contig_2	sc5p_v2_hs_PBMC_10k_AAACCTGTCTTGAGAC_contig_2	GGAGTCTCCCTCACCGCCCAGCTGGGATCTCAGGGCTTCATTTTCT...	F	T	IGHV5-51*01		IGHJ3*02	GAGGTGCAGCTGGTGCAGTCTGGAGCA...GAGGTGAAAAAGCCGG...	GAGGTGCAGCTGGTGCAGTCTGGAGCA...GAGGTGAAAAAGCCCG...	TGTGCGAGACATATCCGTGGGAACAGATTTGGCAATGATGCTTTTG...	...	IGHJ3*02	1.0	437.0	486.0	0.0	0	F	standard	B_VDJ_38_4_4_VJ_191_1_1	322_2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
sc5p_v2_hs_PBMC_1k_TTCCCAGAGTACATGA_contig_2	sc5p_v2_hs_PBMC_1k_TTCCCAGAGTACATGA_contig_2	AGCTCTGAGAGAGGAGCCCAGCCCTGGGATTTTCAGGTGTTTTCAT...	F	T	IGHV3-23*01,IGHV3-23D*01	IGHD1-26*01	IGHJ4*02	GAGGTCCAACTGTTGGAATCTGGGGGA...GGGTTGATACAGCCGG...	GAGGTGCAGCTGTTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...	TGTGCGAGAGTTTTTGGGTCGGTGGGAGCTACTCGTTCTACGGACT...	...	IGHJ4*02	1.0	464	503	0.0	33	F	standard	B_VDJ_184_9_12_VJ_27_1_2	656_755
sc5p_v2_hs_PBMC_1k_TTGAACGCAGGCTGAA_contig_1	sc5p_v2_hs_PBMC_1k_TTGAACGCAGGCTGAA_contig_1	AGGAGTCAGACCCTGTCAGGACACAGCATAGACATGAGGGTCCCCG...	F	T	IGKV1-8*01		IGKJ1*01	GCCATCCGGATGACCCAGTCTCCATCCTCATTCTCTGCATCTACAG...	GCCATCCGGATGACCCAGTCTCCATCCTCATTCTCTGCATCTACAG...	TGTCAACAGTATTATAGTTACCCGTGGACGTTC	...	IGKJ1*01	1.0	378	415	0.0	0	F	standard	B_VDJ_12_11_2_VJ_80_2_4	920_706
sc5p_v2_hs_PBMC_1k_TTGAACGCAGGCTGAA_contig_2	sc5p_v2_hs_PBMC_1k_TTGAACGCAGGCTGAA_contig_2	CGAGCCCAGCACTGGAAGTCGCCGGTGTTTCCATTCGGTGATCATC...	F	T	IGHV3-30-3*01	IGHD3-9*01	IGHJ4*02	CAGGTGCAGCTGGTGGAGTCTGGGGGA...GGCGTGGTCCAGCCTG...	CAGGTGCAGCTGGTGGAGTCTGGGGGA...GGCGTGGTCCAGCCTG...	TGTGCGAGAGATGAGTTAGATATTTTGACTGGTTACAATATCCCAA...	...	IGHJ4*02	1.0	469	509	0.0	0	F	standard	B_VDJ_12_11_2_VJ_80_2_4	920_706
sc5p_v2_hs_PBMC_1k_TTGCCGTAGAATGTGT_contig_1	sc5p_v2_hs_PBMC_1k_TTGCCGTAGAATGTGT_contig_1	GAGCTACAACAGGCAGGCAGGGGCAGCAAGATGGTGTTGCAGACCC...	F	T	IGKV4-1*01		IGKJ2*01	GACATCGTGATGACCCAGTCTCCAGACTCCCTGGCTGTGTCTCTGG...	GACATCGTGATGACCCAGTCTCCAGACTCCCTGGCTGTGTCTCTGG...	TGTCAGCAATATTATAGTACTCCGTACACTTTT	...	IGKJ2*01	1.0	393	430	0.0	0	F	standard	B_VDJ_51_1_1_VJ_196_2_15	1210_756
sc5p_v2_hs_PBMC_1k_TTGCCGTAGAATGTGT_contig_2	sc5p_v2_hs_PBMC_1k_TTGCCGTAGAATGTGT_contig_2	TGGGGAGTGACTCCTGTGCCCCACCATGGACACACTTTGCTCCACG...	F	T	IGHV2-5*02		IGHJ6*02	CAGATCACCTTGAAGGAGTCTGGTCCT...ACGCTGGTGAAACCCA...	CAGATCACCTTGAAGGAGTCTGGTCCT...ACGCTGGTGAAACCCA...	TGTGCACACAGCGACTACTATGAGGGGCGCGGTATGGACGTCTGG	...	IGHJ6*02	1.0	400	445	0.0	0	F	standard	B_VDJ_51_1_1_VJ_196_2_15	1210_756

1808 rows × 124 columns

# create a new Dandelion class with this subset
vdj2 = ddl.Dandelion(subset_data)
vdj2

Dandelion class object with n_obs = 771 and n_contigs = 1808
    data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'c_call', 'junction_length', 'np1_length', 'np2_length', 'v_sequence_start', 'v_sequence_end', 'v_germline_start', 'v_germline_end', 'd_sequence_start', 'd_sequence_end', 'd_germline_start', 'd_germline_end', 'j_sequence_start', 'j_sequence_end', 'j_germline_start', 'j_germline_end', 'v_score', 'v_identity', 'v_support', 'd_score', 'd_identity', 'd_support', 'j_score', 'j_identity', 'j_support', 'fwr1', 'fwr2', 'fwr3', 'fwr4', 'cdr1', 'cdr2', 'cdr3', 'cell_id', 'consensus_count', 'duplicate_count', 'v_call_10x', 'd_call_10x', 'j_call_10x', 'junction_10x', 'junction_10x_aa', 'j_support_igblastn', 'j_score_igblastn', 'j_call_igblastn', 'j_call_blastn', 'j_identity_blastn', 'j_alignment_length_blastn', 'j_number_of_mismatches_blastn', 'j_number_of_gap_openings_blastn', 'j_sequence_start_blastn', 'j_sequence_end_blastn', 'j_germline_start_blastn', 'j_germline_end_blastn', 'j_support_blastn', 'j_score_blastn', 'j_sequence_alignment_blastn', 'j_germline_alignment_blastn', 'j_source', 'd_support_igblastn', 'd_score_igblastn', 'd_call_igblastn', 'd_call_blastn', 'd_identity_blastn', 'd_alignment_length_blastn', 'd_number_of_mismatches_blastn', 'd_number_of_gap_openings_blastn', 'd_sequence_start_blastn', 'd_sequence_end_blastn', 'd_germline_start_blastn', 'd_germline_end_blastn', 'd_support_blastn', 'd_score_blastn', 'd_sequence_alignment_blastn', 'd_germline_alignment_blastn', 'd_source', 'v_call_genotyped', 'germline_alignment_d_mask', 'sample_id', 'c_sequence_alignment', 'c_germline_alignment', 'c_sequence_start', 'c_sequence_end', 'c_score', 'c_identity', 'c_call_10x', 'junction_aa_length', 'fwr1_aa', 'fwr2_aa', 'fwr3_aa', 'fwr4_aa', 'cdr1_aa', 'cdr2_aa', 'cdr3_aa', 'sequence_alignment_aa', 'v_sequence_alignment_aa', 'd_sequence_alignment_aa', 'j_sequence_alignment_aa', 'complete_vdj', 'j_call_multimappers', 'j_call_multiplicity', 'j_call_sequence_start_multimappers', 'j_call_sequence_end_multimappers', 'j_call_support_multimappers', 'mu_count', 'ambiguous', 'rearrangement_status', 'clone_id', 'changeo_clone_id'
    metadata: 'clone_id', 'clone_id_by_size', 'sample_id', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_genotyped_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_genotyped_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_genotyped_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_genotyped_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'

store_germline_reference

Note

From 0.2.4 onward, this is will replace update_germline as the name was misleading.

We can store the corrected germline fasta files (after running TIgGER) in the Dandelion class as a dictionary.

# update the germline using the corrected files after tigger
vdj2.store_germline_reference(
    corrected="tutorial_scgp1/tutorial_scgp1_heavy_igblast_db-pass_genotype.fasta",
    germline=None,
    org="human",
)

Updating germline reference
 finished: Updated Dandelion object:
   'germline', updated germline reference
 (0:00:00)

pp.create_germlines

Then we run pp.create_germline to (re)create the germline_alignment_d_mask column in the data. This works by calling CreateGermlines.py with only -d and -r options. Add further arguments with additional_args like below for your needs. See https://changeo.readthedocs.io/en/stable/examples/germlines.html for more info.

ddl.pp.create_germlines(vdj2, additional_args=["--vf", "v_call_genotyped"])

Reconstructing germline sequences
Running command: CreateGermlines.py -d /var/folders/_r/j_8_fj3x28n2th3ch0ckn9c40000gt/T/tmph34fx0x4/tmp.tsv -r /var/folders/_r/j_8_fj3x28n2th3ch0ckn9c40000gt/T/tmph34fx0x4/germ.fasta --vf v_call_genotyped

     START> CreateGermlines
      FILE> tmp.tsv
GERM_TYPES> dmask
 SEQ_FIELD> sequence_alignment
   V_FIELD> v_call_genotyped
   D_FIELD> d_call
   J_FIELD> j_call
    CLONED> False

PROGRESS> 00:05:31 |####################| 100% (1,808) 0.0 min

 OUTPUT> tmp_germ-pass.tsv
RECORDS> 1808
   PASS> 1808
   FAIL> 0
    END> CreateGermlines

 finished: Returning Dandelion object:
 (0:00:02)

Dandelion class object with n_obs = 771 and n_contigs = 1808
    data: 'sequence_id', 'sequence', 'rev_comp', 'productive', 'v_call', 'd_call', 'j_call', 'sequence_alignment', 'germline_alignment', 'junction', 'junction_aa', 'v_cigar', 'd_cigar', 'j_cigar', 'stop_codon', 'vj_in_frame', 'locus', 'c_call', 'junction_length', 'np1_length', 'np2_length', 'v_sequence_start', 'v_sequence_end', 'v_germline_start', 'v_germline_end', 'd_sequence_start', 'd_sequence_end', 'd_germline_start', 'd_germline_end', 'j_sequence_start', 'j_sequence_end', 'j_germline_start', 'j_germline_end', 'v_score', 'v_identity', 'v_support', 'd_score', 'd_identity', 'd_support', 'j_score', 'j_identity', 'j_support', 'fwr1', 'fwr2', 'fwr3', 'fwr4', 'cdr1', 'cdr2', 'cdr3', 'cell_id', 'consensus_count', 'duplicate_count', 'c_sequence_alignment', 'c_germline_alignment', 'c_sequence_start', 'c_sequence_end', 'c_score', 'c_identity', 'junction_aa_length', 'fwr1_aa', 'fwr2_aa', 'fwr3_aa', 'fwr4_aa', 'cdr1_aa', 'cdr2_aa', 'cdr3_aa', 'sequence_alignment_aa', 'v_sequence_alignment_aa', 'd_sequence_alignment_aa', 'j_sequence_alignment_aa', 'complete_vdj', 'clone_id', 'v_call_10x', 'd_call_10x', 'j_call_10x', 'junction_10x', 'junction_10x_aa', 'j_support_igblastn', 'j_score_igblastn', 'j_call_igblastn', 'j_call_blastn', 'j_identity_blastn', 'j_alignment_length_blastn', 'j_number_of_mismatches_blastn', 'j_number_of_gap_openings_blastn', 'j_sequence_start_blastn', 'j_sequence_end_blastn', 'j_germline_start_blastn', 'j_germline_end_blastn', 'j_support_blastn', 'j_score_blastn', 'j_sequence_alignment_blastn', 'j_germline_alignment_blastn', 'j_source', 'd_support_igblastn', 'd_score_igblastn', 'd_call_igblastn', 'd_call_blastn', 'd_identity_blastn', 'd_alignment_length_blastn', 'd_number_of_mismatches_blastn', 'd_number_of_gap_openings_blastn', 'd_sequence_start_blastn', 'd_sequence_end_blastn', 'd_germline_start_blastn', 'd_germline_end_blastn', 'd_support_blastn', 'd_score_blastn', 'd_sequence_alignment_blastn', 'd_germline_alignment_blastn', 'd_source', 'v_call_genotyped', 'germline_alignment_d_mask', 'sample_id', 'c_call_10x', 'j_call_multimappers', 'j_call_multiplicity', 'j_call_sequence_start_multimappers', 'j_call_sequence_end_multimappers', 'j_call_support_multimappers', 'mu_count', 'ambiguous', 'rearrangement_status', 'changeo_clone_id'
    metadata: 'clone_id', 'clone_id_by_size', 'sample_id', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_genotyped_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_genotyped_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_genotyped_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_genotyped_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'

Ensure that the germline_alignment_d_mask column is populated or subsequent steps will fail.

vdj2.data[["v_call_genotyped", "germline_alignment_d_mask"]]

	v_call_genotyped	germline_alignment_d_mask
sequence_id
sc5p_v2_hs_PBMC_10k_AAACCTGTCCGTTGTC_contig_2	IGHV1-69*01,IGHV1-69D*01	CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...
sc5p_v2_hs_PBMC_10k_AAACCTGTCCGTTGTC_contig_1	IGKV1-8*01	GCCATCCGGATGACCCAGTCTCCATCCTCATTCTCTGCATCTACAG...
sc5p_v2_hs_PBMC_10k_AAACCTGTCGAGAACG_contig_1	IGLV5-45*02	CAGGCTGTGCTGACTCAGCCGTCTTCC...CTCTCTGCATCTCCTG...
sc5p_v2_hs_PBMC_10k_AAACCTGTCGAGAACG_contig_2	IGHV1-2*02	CAGGTGCAGCTGGTGCAGTCTGGGGCT...GAGGTGAAGAAGCCTG...
sc5p_v2_hs_PBMC_10k_AAACCTGTCTTGAGAC_contig_2	IGHV5-51*03	GAGGTGCAGCTGGTGCAGTCTGGAGCA...GAGGTGAAAAAGCCGG...
...	...	...
sc5p_v2_hs_PBMC_1k_TTCCCAGAGTACATGA_contig_2	IGHV3-23*01,IGHV3-23D*01	GAGGTGCAGCTGTTGGAGTCTGGGGGA...GGCTTGGTACAGCCTG...
sc5p_v2_hs_PBMC_1k_TTGAACGCAGGCTGAA_contig_1	IGKV1-8*01	GCCATCCGGATGACCCAGTCTCCATCCTCATTCTCTGCATCTACAG...
sc5p_v2_hs_PBMC_1k_TTGAACGCAGGCTGAA_contig_2	IGHV3-30-3*01	CAGGTGCAGCTGGTGGAGTCTGGGGGA...GGCGTGGTCCAGCCTG...
sc5p_v2_hs_PBMC_1k_TTGCCGTAGAATGTGT_contig_1	IGKV4-1*01	GACATCGTGATGACCCAGTCTCCAGACTCCCTGGCTGTGTCTCTGG...
sc5p_v2_hs_PBMC_1k_TTGCCGTAGAATGTGT_contig_2	IGHV2-5*02	CAGATCACCTTGAAGGAGTCTGGTCCT...ACGCTGGTGAAACCCA...

1808 rows × 2 columns

The default behaviour is to mask the D region with Ns with option.

pp.quantify_mutations

The options for pp.quantify_mutations are the same as the basic mutational load analysis vignette [Gupta2015]. The default behavior is to sum all mutations scores (heavy and light chains, silent and replacement mutations) for the same cell.

Again, this function can be run immediately after pp.reassign_alleles on the genotyped .tsv files (without loading into pandas or Dandelion). Here I’m illustrating a few other options that may be useful.

# switching back to using the full vdj object
ddl.pp.quantify_mutations(vdj)

Quantifying mutations
 finished: Updated Dandelion object:
   'data', contig-indexed AIRR table
   'metadata', cell-indexed observations table
 (0:00:12)

ddl.pp.quantify_mutations(vdj, combine=False)

Quantifying mutations
 finished: Updated Dandelion object:
   'data', contig-indexed AIRR table
   'metadata', cell-indexed observations table
 (0:00:06)

Specifying split_locus = True will split up the results for the different chains.

ddl.pp.quantify_mutations(vdj, split_locus=True)

Quantifying mutations
 finished: Updated Dandelion object:
   'data', contig-indexed AIRR table
   'metadata', cell-indexed observations table
 (0:00:07)

To update the AnnData object, simply rerun tl.transfer.

ddl.tl.transfer(adata, vdj)

Transferring network
converting matrices
Updating anndata slots
 finished: updated `.obs` with `.metadata`
added to `.uns['neighbors']` and `.uns['clone_id']`
and `.obsp`
   'distances', clonotype-weighted adjacency matrix
   'connectivities', clonotype-weighted adjacency matrix (0:00:07)

adata

AnnData object with n_obs × n_vars = 22985 × 1464
    obs: 'sampleid', 'batch', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'gmm_pct_count_clusters_keep', 'scrublet_score', 'is_doublet', 'filter_rna', 'has_contig', 'sample_id', 'locus_VDJ', 'locus_VJ', 'productive_VDJ', 'productive_VJ', 'v_call_genotyped_VDJ', 'd_call_VDJ', 'j_call_VDJ', 'v_call_genotyped_VJ', 'j_call_VJ', 'c_call_VDJ', 'c_call_VJ', 'junction_VDJ', 'junction_VJ', 'junction_aa_VDJ', 'junction_aa_VJ', 'v_call_genotyped_B_VDJ', 'd_call_B_VDJ', 'j_call_B_VDJ', 'v_call_genotyped_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', 'leiden', 'clone_id', 'clone_id_by_size', 'changeo_clone_id', 'mu_count', 'mu_count_seq_r', 'mu_count_seq_s', 'mu_count_seq_r_IGK', 'mu_count_seq_s_IGK', 'mu_count_IGK', 'mu_count_seq_r_IGL', 'mu_count_seq_s_IGL', 'mu_count_IGL', 'mu_count_seq_r_IGH', 'mu_count_seq_s_IGH', 'mu_count_IGH'
    var: 'feature_types', 'genome', 'gene_ids-0', 'gene_ids-1', 'gene_ids-2', 'gene_ids-3', 'n_cells', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
    uns: 'chain_status_colors', 'clone_id', 'hvg', 'leiden', 'leiden_colors', 'locus_status_colors', 'log1p', 'neighbors', 'pca', 'rna_neighbors', 'sampleid_colors', 'umap'
    obsm: 'X_pca', 'X_umap', 'X_vdj'
    varm: 'PCs'
    obsp: 'connectivities', 'distances', 'rna_connectivities', 'rna_distances', 'vdj_connectivities', 'vdj_distances'

from scanpy.plotting.palettes import default_28, default_102

sc.set_figure_params(figsize=[4, 4])
ddl.pl.clone_network(
    adata,
    color=[
        "clone_id",
        "mu_count",
        "mu_count_seq_r",
        "mu_count_seq_s",
        "mu_count_IGH",
        "mu_count_IGL",
    ],
    ncols=2,
    legend_loc="none",
    legend_fontoutline=3,
    edges_width=1,
    palette=default_28 + default_102,
    color_map="viridis",
    size=20,
)

WARNING: Length of palette colors is smaller than the number of categories (palette length: 130, categories length: 2027. Some categories will have the same color.

Calculating diversity

Disclaimer: the functions here are experimental. Please look to other sources/methods for doing this properly. Also, would appreciate any help to help me finalise this!

tl.clone_rarefaction and pl.clone_rarefaction

We can use pl.clone_rarefaction to generate rarefaction curves for the clones. tl.clone_rarefaction will populate the .uns slot with the results. groupby option must be specified. In this case, I decided to group by sample. The function will only work on an AnnData object and not a Dandelion object.

ddl.pl.clone_rarefaction(adata, color="sampleid")

removing due to zero counts:

Calculating rarefaction curve : 100%|██████████| 4/4 [00:00<00:00, 13.48it/s]

<Figure Size: (500 x 300)>

ddl.tl.clone_diversity

tl.clone_diversity allows for calculation of diversity measures such as Chao1, Shannon Entropy and Gini indices.

While the function can work on both AnnData and Dandelion objects, the methods for gini index calculation will only work on a Dandelion object as it requires access to the network.

For Gini indices, we provide several types of measures, inspired by bulk BCRseq analysis methods from [Bashford-Rogers2013]:

The following two indices are returned with metric="clone_network".

1. network cluster/clone size Gini index

In a contracted BCR network (where identical BCRs are collapsed into the same node/vertex), disparity in the distribution should be correlated to the amount of mutation events i.e. larger networks should indicate more mutation events and smaller networks should indicate lesser mutation events.

2. network vertex/node size Gini index

In the same contracted network, we can count the number of merged/contracted nodes; nodes with higher count numbers indicate more clonal expansion. Thus, disparity in the distribution of count numbers (referred to as vertex size) should be correlated to the overall clonality i.e. clones with larger vertex sizes are more monoclonal and clones with smaller vertex sizes are more polyclonal.

Therefore, a Gini index of 1 on either measures repesents perfect inequality (i.e. monoclonal and highly mutated) and a value of 0 represents perfect equality (i.e. polyclonal and unmutated).

Note

However, there are a few limitations/challenges that comes with single-cell data:

• 1. In the process of contracting the network, we discard the single-cell level information.

• 2. Contraction of network is very slow, particularly when there is a lot of clonally-related cells.

• 3. For the full implementation and interpretation of both measures, although more evident with cluster/clone size, it requires the BCR repertoire to be reasonably/deeply sampled and we know that this is currently limited by the low recovery from single cell data with current technologies.

Therefore, we implement a few work around options, and ‘experimental’ options below, to try and circumvent these issues.

Firstly, as a work around for (C), the cluster size gini index can be calculated before or after network contraction. If performing before network contraction (default), it will be calculated based on the size of subgraphs of connected components in the main graph. This will retain the single-cell information and should appropriately show the distribution of the data. If performing after network contraction, the calculation is performed after network contraction, achieving the same effect as the method for bulk BCR-seq as described above. This option can be toggled by use_contracted and only applies to network cluster size gini index calculation.

3. clone centrality Gini index - metric="clone_centrality"

Node/vertex closeness centrality indicates how tightly packed clones are (more clonally related) and thus the distribution of the number of cells connected in each clone informs on whether clones in general are more monoclonal or polyclonal.

4. clone degree Gini index - metric="clone_degree"

Node/vertex degree indicates how many cells are connected to an individual cell, another indication of how clonally related cells are. However, this would also highlight cells that are in the middle of large networks but are not necessarily within clonally expanded regions (e.g. intermediate connecting cells within the minimum spanning tree).

22. clone size Gini index - metric="clone_size"

This is not to be confused with the network cluster size gini index calculation above as this doesn’t rely on the network, although the values should be similar. This is just a simple implementation based on the data frame for the relevant clone_id column. By default, this metric is also returned when running metric=clone_centrality or metric=clone_degree.

Note

For (I) and (II), we can specify expanded_only option to compute the statistic for all clones or expanded only clones. Unlike options (I) and (II), the current calculation for (III) and (IV) is largely influenced by the amount of expanded clones i.e. clones with at least 2 cells, and not affected by the number of singleton clones because singleton clones will have a value of 0 regardless.

The diversity functions also have the option to perform downsampling to a fixed number of cells, or to the smallest sample size specified via groupby (default) so that sample sizes are even when comparing between groups.

if update_obs_meta=False, a data frame is returned; otherwise, the value gets added to the AnnData.obs or Dandelion.metadata accordingly.

sc.settings.verbosity = 1  # it gets very noisy
ddl.tl.clone_diversity(
    vdj, groupby="sample_id", method="gini", metric="clone_network"
)
ddl.tl.clone_diversity(
    vdj, groupby="sample_id", method="gini", metric="clone_centrality"
)
ddl.tl.transfer(adata, vdj)

ddl.pl.clone_network(
    adata,
    color=[
        "clone_network_cluster_size_gini",
        "clone_network_vertex_size_gini",
        "clone_size_gini",
        "clone_centrality_gini",
    ],
    ncols=2,
    size=20,
)

With these particular samples, because there is not many expanded clones in general, the gini indices are quite low when calculated within each sample. We can re-run it by specifying expanded_only = True to only factor in expanded clones. We also specify the key_added option to create a new column instead of writing over the original columns.

ddl.tl.clone_diversity(
    vdj,
    groupby="sample_id",
    method="gini",
    metric="clone_network",
    expanded_only=True,
    key_added=[
        "clone_network_cluster_size_gini_expanded",
        "clone_network_vertex_size_gini_expanded",
    ],
)
ddl.tl.transfer(adata, vdj)

ddl.pl.clone_network(
    adata,
    color=[
        "clone_network_cluster_size_gini_expanded",
        "clone_network_vertex_size_gini_expanded",
    ],
    ncols=2,
    size=20,
)

We can also choose not to update the metadata to return a pandas dataframe.

gini = ddl.tl.clone_diversity(
    vdj, groupby="sample_id", method="gini", update_obs_meta=False
)
gini

	clone_network_cluster_size_gini	clone_network_vertex_size_gini
vdj_v1_hs_pbmc3	0.004237	0.001415
sc5p_v2_hs_PBMC_10k	0.007108	0.000952
vdj_nextgem_hs_pbmc3	0.043968	0.014073
sc5p_v2_hs_PBMC_1k	0.027772	0.000000

gini2 = ddl.tl.clone_diversity(
    vdj,
    groupby="sample_id",
    method="gini",
    update_obs_meta=False,
    expanded_only=True,
    key_added=[
        "clone_network_cluster_size_gini_expanded",
        "clone_network_vertex_size_gini_expanded",
    ],
)
gini2

	clone_network_cluster_size_gini_expanded	clone_network_vertex_size_gini_expanded
vdj_v1_hs_pbmc3	0.000000	0.333333
sc5p_v2_hs_PBMC_10k	0.200000	0.083333
vdj_nextgem_hs_pbmc3	0.289116	0.208333
sc5p_v2_hs_PBMC_1k	0.000000	0.000000

import seaborn as sns
import matplotlib.pyplot as plt

p = sns.scatterplot(
    x="clone_network_cluster_size_gini",
    y="clone_network_vertex_size_gini",
    data=gini,
    hue=gini.index,
    palette=dict(
        zip(adata.obs["sampleid"].cat.categories, adata.uns["sampleid_colors"])
    ),
)
p.set(ylim=(-0.1, 1), xlim=(-0.1, 1))
plt.legend(bbox_to_anchor=(1, 0.5), loc="center left", frameon=False)
p

<Axes: xlabel='clone_network_cluster_size_gini', ylabel='clone_network_vertex_size_gini'>

p2 = sns.scatterplot(
    x="clone_network_cluster_size_gini_expanded",
    y="clone_network_vertex_size_gini_expanded",
    data=gini2,
    hue=gini2.index,
    palette=dict(
        zip(adata.obs["sampleid"].cat.categories, adata.uns["sampleid_colors"])
    ),
)
p2.set(ylim=(-0.1, 1), xlim=(-0.1, 1))
plt.legend(bbox_to_anchor=(1, 0.5), loc="center left", frameon=False)
p2

<Axes: xlabel='clone_network_cluster_size_gini_expanded', ylabel='clone_network_vertex_size_gini_expanded'>

We can also visualise what the results for the clone centrality gini indices.

gini = ddl.tl.clone_diversity(
    vdj,
    groupby="sample_id",
    method="gini",
    metric="clone_centrality",
    update_obs_meta=False,
)
gini

	clone_size_gini	clone_centrality_gini
vdj_v1_hs_pbmc3	0.004237	0.000000
sc5p_v2_hs_PBMC_10k	0.007108	0.000000
vdj_nextgem_hs_pbmc3	0.046251	0.498246
sc5p_v2_hs_PBMC_1k	0.027772	0.000000

# not a great example because there's only 1 big clone in 1 sample.
p = sns.scatterplot(
    x="clone_size_gini",
    y="clone_centrality_gini",
    data=gini,
    hue=gini.index,
    palette=dict(
        zip(adata.obs["sampleid"].cat.categories, adata.uns["sampleid_colors"])
    ),
)
p.set(ylim=(-0.1, 1), xlim=(-0.1, 1))
plt.legend(bbox_to_anchor=(1, 0.5), loc="center left", frameon=False)
p

<Axes: xlabel='clone_size_gini', ylabel='clone_centrality_gini'>

Chao1 is an estimator based on abundance

ddl.tl.clone_diversity(
    vdj, groupby="sample_id", method="chao1", update_obs_meta=False
)

	clone_size_chao1
vdj_v1_hs_pbmc3	55343.000000
sc5p_v2_hs_PBMC_10k	48513.200000
vdj_nextgem_hs_pbmc3	17196.333333
sc5p_v2_hs_PBMC_1k	1243.000000

For Shannon Entropy, we can calculate a normalized (inspired by scirpy’s function) and non-normalized value.

ddl.tl.clone_diversity(
    vdj, groupby="sample_id", method="shannon", update_obs_meta=False
)

	clone_size_normalized_shannon
vdj_v1_hs_pbmc3	0.999867
sc5p_v2_hs_PBMC_10k	0.999665
vdj_nextgem_hs_pbmc3	0.993710
sc5p_v2_hs_PBMC_1k	0.998743

ddl.tl.clone_diversity(
    vdj,
    groupby="sample_id",
    method="shannon",
    update_obs_meta=False,
    normalize=False,
)

	clone_size_shannon
vdj_v1_hs_pbmc3	8.875337
sc5p_v2_hs_PBMC_10k	9.439783
vdj_nextgem_hs_pbmc3	9.566974
sc5p_v2_hs_PBMC_1k	6.121578

That sums it up for now! Let me know if you have any ideas at [z.tuong@uq.edu.au] and I can try and see if i can implement it or we can work something out to collaborate on!