Use 'umi_count' instead of 'duplicate_count'

CHANGELOG.md

@@ -8,6 +8,20 @@ and this project adheres to [Semantic Versioning][].
 [keep a changelog]: https://keepachangelog.com/en/1.0.0/
 [semantic versioning]: https://semver.org/spec/v2.0.0.html
 
+## Unreleased
+
+### Backwards-incompatible changes
+
+-   Use the `umi_count` field instead of `duplicate_count` to store UMI counts. The field `umi_count` has been added to
+    the AIRR Rearrangement standard in [version 1.4](https://docs.airr-community.org/en/latest/news.html#version-1-4-1-august-27-2022).
+    Use of `duplicate_count` for UMI counts is now discouraged. Scirpy will use `umi_count` in all `scirpy.io` functions.
+    It will _not_ change AIRR data that is read through `scirpy.io.read_airr` that still uses the `duplicate_count` column.
+    Scirpy remains compatible with datasets that still use `duplicate_count`. You can update your dataset using
+
+    ```python
+    adata.obsm["airr"]["umi_count"] = adata.obsm["airr"]["duplicate_count"]
+    ```
+
 ## v0.15.0
 
 ### Fixes

pyproject.toml

@@ -35,7 +35,7 @@ dependencies = [
     'igraph != 0.10.0,!=0.10.1',
     'networkx>=2.5',
     'squarify',
-    'airr>=1.2',
+    'airr>=1.4.1',
     'tqdm>=4.63', # https://github.com/tqdm/tqdm/issues/1082
     'adjustText>=0.7',
     'numba>=0.41.0',

src/scirpy/io/_io.py

@@ -6,7 +6,7 @@
 from collections.abc import Collection, Iterable, Sequence
 from glob import iglob
 from pathlib import Path
-from typing import Any, Literal, Union
+from typing import Any, Union
 
 import numpy as np
 import pandas as pd
@@ -125,7 +125,7 @@ def _read_10x_vdj_json(
         chain["locus"] = chain_type
         chain["junction"] = contig["cdr3_seq"]
         chain["junction_aa"] = contig["cdr3"]
-        chain["duplicate_count"] = contig["umi_count"]
+        chain["umi_count"] = contig["umi_count"]
         chain["consensus_count"] = contig["read_count"]
         chain["productive"] = contig["productive"]
         chain["is_cell"] = contig["is_cell"]
@@ -166,7 +166,7 @@ def _read_10x_vdj_csv(
                 locus=chain_series["chain"],
                 junction_aa=chain_series["cdr3"],
                 junction=chain_series["cdr3_nt"],
-                duplicate_count=chain_series["umis"],
+                umi_count=chain_series["umis"],
                 consensus_count=chain_series["reads"],
                 productive=_is_true2(chain_series["productive"]),
                 v_call=chain_series["v_gene"],
@@ -352,7 +352,7 @@ def _process_chains(chains, chain_type):
 )
 def read_airr(
     path: Union[str, Sequence[str], Path, Sequence[Path], pd.DataFrame, Sequence[pd.DataFrame]],
-    use_umi_count_col: Union[bool, Literal["auto"]] = "auto",
+    use_umi_count_col: None = None,  # deprecated, kept for backwards-compatibility
     infer_locus: bool = True,
     cell_attributes: Collection[str] = DEFAULT_AIRR_CELL_ATTRIBUTES,
     include_fields: Any = None,
@@ -380,10 +380,9 @@ def read_airr(
         as a List, e.g. `["path/to/tcr_alpha.tsv", "path/to/tcr_beta.tsv"]`.
         Alternatively, this can be a pandas data frame.
     use_umi_count_col
-        Whether to add UMI counts from the non-strandard (but common) `umi_count`
-        column. When this column is used, the UMI counts are moved over to the
-        standard `duplicate_count` column. Default: Use `umi_count` if there is
-        no `duplicate_count` column present.
+        Deprecated, has no effect as of v0.16. Since v1.4 of the AIRR standard, `umi_count`
+        is an official field in the Rearrangement schema and preferred over `duplicate_count`.
+        `umi_count` now always takes precedence over `duplicate_count`.
     infer_locus
         Try to infer the `locus` column from gene names, in case it is not specified.
     cell_attributes
@@ -409,16 +408,6 @@ def read_airr(
     if isinstance(path, (str, Path, pd.DataFrame)):
         path: list[Union[str, Path, pd.DataFrame]] = [path]  # type: ignore
 
-    def _decide_use_umi_count_col(chain_dict):
-        """Logic to decide whether or not to use counts form the `umi_counts` column."""
-        if "umi_count" in chain_dict and use_umi_count_col == "auto" and "duplicate_count" not in chain_dict:
-            logger.warning("Renaming the non-standard `umi_count` column to `duplicate_count`. ")  # type: ignore
-            return True
-        elif use_umi_count_col is True:
-            return True
-        else:
-            return False
-
     for tmp_path_or_df in path:
         if isinstance(tmp_path_or_df, pd.DataFrame):
             iterator = _read_airr_rearrangement_df(tmp_path_or_df)
@@ -438,9 +427,6 @@ def _decide_use_umi_count_col(chain_dict):
                 )
                 airr_cells[cell_id] = tmp_cell
 
-            if _decide_use_umi_count_col(chain_dict):
-                chain_dict["duplicate_count"] = get_rearrangement_schema().to_int(chain_dict.pop("umi_count"))
-
             if infer_locus and "locus" not in chain_dict:
                 logger.warning(
                     "`locus` column not found in input data. The locus is being inferred from the {v,d,j,c}_call columns."
@@ -742,7 +728,7 @@ def _get(row, field):
                 "junction_aa": _get(row, "CDR3_Translation"),
                 "productive": row["Productive"],
                 "consensus_count": row["Read_Count"],
-                "duplicate_count": row["Molecule_Count"],
+                "umi_count": row["Molecule_Count"],
             }
         )
         tmp_cell.add_chain(tmp_chain)

src/scirpy/pp/_index_chains.py

@@ -27,7 +27,9 @@ def index_chains(
         "require_junction_aa",
     ),
     sort_chains_by: Mapping[str, Any] = MappingProxyType(
-        {"duplicate_count": 0, "consensus_count": 0, "junction": "", "junction_aa": ""}
+        # Since AIRR version v1.4.1, `duplicate_count` is deprecated in favor of `umi_count`.
+        # We still keep it as sort key for backwards compatibility
+        {"umi_count": 0, "duplicate_count": 0, "consensus_count": 0, "junction": "", "junction_aa": ""}
     ),
     airr_mod: str = "airr",
     airr_key: str = "airr",
@@ -89,8 +91,12 @@ def index_chains(
 
     # only warn if those fields are in the key (i.e. this should give a warning if those are missing with
     # default settings. If the user specifies their own dictionary, they are on their own)
-    if "duplicate_count" in sort_chains_by and "consensus_count" in sort_chains_by:
-        if "duplicate_count" not in params.airr.fields and "consensus_count" not in params.airr.fields:
+    if "duplicate_count" in sort_chains_by and "consensus_count" in sort_chains_by and "umi_count" in sort_chains_by:
+        if (
+            "duplicate_count" not in params.airr.fields
+            and "consensus_count" not in params.airr.fields
+            and "umi_count" not in sort_chains_by
+        ):
             logging.warning("No expression information available. Cannot rank chains by expression. ")  # type: ignore
 
     if "locus" not in params.airr.fields:

src/scirpy/tests/data/airr/rearrangement_ig.tsv

@@ -1,4 +1,4 @@
-cell_id	clone_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
+cell_id	clone_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	umi_count	is_cell
 AAACCTGAGGAGTCTG-1		AAACCTGAGGAGTCTG-1_contig_1	ACAACCACACCCCTCCTAAGAAGAAGCCCCTAGACCACAGCTCCACACCATGGACTGGACCTGGAGGATCCTCTTCTTGGTGGCAGCAGCAACAGGTGCCCACTCCCAGGTGCAGCTGGTGCAATCTGGGTCTGAGTTGAAGAAGCCTGGGGCCTCAGTGAAGGTTTCCTGCAAGGCTTCTGGATACACCTTCACTAGCTATGCTATGAATTGGGTGCGACAGGCCCCTGGACAAGGGCTTGAGTGGATGGGATGGATCAACACCAACACTGGGAACCCAACGTATGCCCAGGGCTTCACAGGACGGTTTGTCTTCTCCTTGGACACCTCTGTCAGCACGGCATATCTGCAGATCAGCAGCCTAAAGGCTGAGGACACTGCCGTGTATTACTGTGCGAGCCTCTGGCAAGATGCCAGTGGATACAGCTATGGTAAATACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAGGGAGTGCATCCGCCCCAACCCTTTTCCCCCTCGTCTCCTGTGAGAATTCCCCGTCGGATACGAGCAGCGTG	MDWTWRILFLVAAATGAHSQVQLVQSGSELKKPGASVKVSCKASGYTFTSYAMNWVRQAPGQGLEWMGWINTNTGNPTYAQGFTGRFVFSLDTSVSTAYLQISSLKAEDTAVYYCASLWQDASGYSYGKYYYYYGMDVWGQGTTVTVSSGSASAPTLFPLVSCENSPSDTSSV	T	F	IGHV7-4-1	49S353M166S			IGHJ6	434S63M71S	IGHM	497S71M	ACAACCACACCCCTCCTAAGAAGAAGCCCCTAGACCACAGCTCCACACCATGGACTGGACCTGGAGGATCCTCTTCTTGGTGGCAGCAGCAACAGGTGCCCACTCCCAGGTGCAGCTGGTGCAATCTGGGTCTGAGTTGAAGAAGCCTGGGGCCTCAGTGAAGGTTTCCTGCAAGGCTTCTGGATACACCTTCACTAGCTATGCTATGAATTGGGTGCGACAGGCCCCTGGACAAGGGCTTGAGTGGATGGGATGGATCAACACCAACACTGGGAACCCAACGTATGCCCAGGGCTTCACAGGACGGTTTGTCTTCTCCTTGGACACCTCTGTCAGCACGGCATATCTGCAGATCAGCAGCCTAAAGGCTGAGGACACTGCCGTGTATTACTGTGCGAGCCTCTGGCAAGATGCCAGTGGATACAGCTATGGTAAATACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAGGGAGTGCATCCGCCCCAACCCTTTTCCCCCTCGTCTCCTGTGAGAATTCCCCGTCGGATACGAGCAGCGTG	ACAACCACACCCCTCCTAAGAAGAAGCCCCTAGACCACAGCTCCACACCATGGACTGGACCTGGAGGATCCTCTTCTTGGTGGCAGCAGCAACAGGTGCCCACTCCCAGGTGCAGCTGGTGCAATCTGGGTCTGAGTTGAAGAAGCCTGGGGCCTCAGTGAAGGTTTCCTGCAAGGCTTCTGGATACACCTTCACTAGCTATGCTATGAATTGGGTGCGACAGGCCCCTGGACAAGGGCTTGAGTGGATGGGATGGATCAACACCAACACTGGGAACCCAACGTATGCCCAGGGCTTCACAGGACGGTTTGTCTTCTCCTTGGACACCTCTGTCAGCACGGCATATCTGCAGATCTGCAGCCTAAAGGCTGAGGACACTGCCGTGTATTACTGTGCGAGAGAATTACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAGGGAGTGCATCCGCCCCAACCCTTTTCCCCCTCGTCTCCTGTGAGAATTCCCCGTCGGATACGAGCAGCGTG	TGTGCGAGCCTCTGGCAAGATGCCAGTGGATACAGCTATGGTAAATACTACTACTACTACGGTATGGACGTCTGG	CASLWQDASGYSYGKYYYYYGMDVW	75	25	50	402			435	497	498	568	791	7	T
 AAACCTGAGGAGTCTG-1		AAACCTGAGGAGTCTG-1_contig_2	AGAGCTCTGGAGAAGAGCTGCTCAGTTAGGACCCAGAGGGAACCATGGAAACCCCAGCGCAGCTTCTCTTCCTCCTGCTACTCTGGCTCCCAGATACCACCGGAGAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAGGGGAAAGAGCCACCCTCTCCTGCAGGGCCAGTCAGAGTGTTAGCAGCAGCTACTTAGCCTGGTACCAGCAGAAACCTGGCCAGGCTCCCAGGCTCCTCATCTATGGTGCATCCAGCAGGGCCACTGGCATCCCAGACAGGTTCAGTGGCAGTGGGTCTGGGACAGACTTCACTCTCACCATCAGCAGACTGGAGCCTGAAGATTTTGCAGTGTATTACTGTCAGCAGTATGGTAGCTCACTTACGTGGACGTTCGGCCAAGGGACCAAGGTGGAAATCAAACGAACTGTGGCTGCACCATCTGTCTTCATCTTCCCGCCATCTGATGAGCAGTTGAAATCTGGAACTGCCTCTGTTGTGTGCCTGCTGAATAACTTCTATCCCAGAGAGGCCAAAGTACAGTGGAAGGTGGATAACGC	METPAQLLFLLLLWLPDTTGEIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSLTWTFGQGTKVEIKRTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDN	T	F	IGKV3-20	44S348M176S			IGKJ1	394S38M136S	IGKC	432S136M	AGAGCTCTGGAGAAGAGCTGCTCAGTTAGGACCCAGAGGGAACCATGGAAACCCCAGCGCAGCTTCTCTTCCTCCTGCTACTCTGGCTCCCAGATACCACCGGAGAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAGGGGAAAGAGCCACCCTCTCCTGCAGGGCCAGTCAGAGTGTTAGCAGCAGCTACTTAGCCTGGTACCAGCAGAAACCTGGCCAGGCTCCCAGGCTCCTCATCTATGGTGCATCCAGCAGGGCCACTGGCATCCCAGACAGGTTCAGTGGCAGTGGGTCTGGGACAGACTTCACTCTCACCATCAGCAGACTGGAGCCTGAAGATTTTGCAGTGTATTACTGTCAGCAGTATGGTAGCTCACTTACGTGGACGTTCGGCCAAGGGACCAAGGTGGAAATCAAACGAACTGTGGCTGCACCATCTGTCTTCATCTTCCCGCCATCTGATGAGCAGTTGAAATCTGGAACTGCCTCTGTTGTGTGCCTGCTGAATAACTTCTATCCCAGAGAGGCCAAAGTACAGTGGAAGGTGGATAACGC	CCTGGGTCAGAGCTCTGGAGAAGAGCTGCTCAGTTAGGACCCAGAGGGAACCATGGAAACCCCAGCGCAGCTTCTCTTCCTCCTGCTACTCTGGCTCCCAGATACCACCGGAGAAATTGTGTTGACGCAGTCTCCAGGCACCCTGTCTTTGTCTCCAGGGGAAAGAGCCACCCTCTCCTGCAGGGCCAGTCAGAGTGTTAGCAGCAGCTACTTAGCCTGGTACCAGCAGAAACCTGGCCAGGCTCCCAGGCTCCTCATCTATGGTGCATCCAGCAGGGCCACTGGCATCCCAGACAGGTTCAGTGGCAGTGGGTCTGGGACAGACTTCACTCTCACCATCAGCAGACTGGAGCCTGAAGATTTTGCAGTGTATTACTGTCAGCAGTATGGTAGCTCACCTGTGGACGTTCGGCCAAGGGACCAAGGTGGAAATCAAACGAACTGTGGCTGCACCATCTGTCTTCATCTTCCCGCCATCTGATGAGCAGTTGAAATCTGGAACTGCCTCTGTTGTGTGCCTGCTGAATAACTTCTATCCCAGAGAGGCCAAAGTACAGTGGAAGGTGGATAACGC	TGTCAGCAGTATGGTAGCTCACTTACGTGGACGTTC	CQQYGSSLTWTF	36	12	45	392			395	432	433	568	5260	41	T
 AAACCTGCAGCGTAAG-1		AAACCTGCAGCGTAAG-1_contig_1	GAGCTCTGGGAGAGGAGCCCAGCACTAGAAGTCGGCGGTGTTTCCATTCGGTGATCAGCACTGAACACAGAGGACTCACCATGGAGTTTGGGCTGAGCTGGGTTTTCCTCGTTGCTCTTTTAAGAGGTGTCCAGTGTCAGGTGCAGCTGGTGGAGTCTGGGGGAGGCGTGGTCCAGCCTGGGAGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTCAGTAGCTATGGCATGCACTGGGTCCGCCAGGCTCCAGGCAAGGGGCTGGAGTGGGTGGCAGTTATATCATATGATGGAAGTAATAAATACTATGCAGACTCCGTGAAGGGCCGATTCACCATCTCCAGAGACAATTCCAAGAACACGCTGTATCTGCAAATGAACAGCCTGAGAGCTGAGGACACGGCTGTGTATTACTGTGCGAAGGGGAGAGTCGTAGTGGGAGCTACTACCCCCAGGAACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAGGGAGTGCATCCGCCCCAACCCTTTTCCCCCTCGTCTCCTGTGAGAATTCCCCGTCGGATACGAGCAGCGTG	MEFGLSWVFLVALLRGVQCQVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKGRVVVGATTPRNYYYYGMDVWGQGTTVTVSSGSASAPTLFPLVSCENSPSDTSSV	T	F	IGHV3-30	80S351M165S			IGHJ6	462S63M71S	IGHM	525S71M	GAGCTCTGGGAGAGGAGCCCAGCACTAGAAGTCGGCGGTGTTTCCATTCGGTGATCAGCACTGAACACAGAGGACTCACCATGGAGTTTGGGCTGAGCTGGGTTTTCCTCGTTGCTCTTTTAAGAGGTGTCCAGTGTCAGGTGCAGCTGGTGGAGTCTGGGGGAGGCGTGGTCCAGCCTGGGAGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTCAGTAGCTATGGCATGCACTGGGTCCGCCAGGCTCCAGGCAAGGGGCTGGAGTGGGTGGCAGTTATATCATATGATGGAAGTAATAAATACTATGCAGACTCCGTGAAGGGCCGATTCACCATCTCCAGAGACAATTCCAAGAACACGCTGTATCTGCAAATGAACAGCCTGAGAGCTGAGGACACGGCTGTGTATTACTGTGCGAAGGGGAGAGTCGTAGTGGGAGCTACTACCCCCAGGAACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAGGGAGTGCATCCGCCCCAACCCTTTTCCCCCTCGTCTCCTGTGAGAATTCCCCGTCGGATACGAGCAGCGTG	CAGCTCTGGGAGAGGAGCCCAGCACTAGAAGTCGGCGGTGTTTCCATTCGGTGATCAGCACTGAACACAGAGGACTCACCATGGAGTTTGGGCTGAGCTGGGTTTTCCTCGTTGCTCTTTTAAGAGGTGTCCAGTGTCAGGTGCAGCTGGTGGAGTCTGGGGGAGGCGTGGTCCAGCCTGGGAGGTCCCTGAGACTCTCCTGTGCAGCCTCTGGATTCACCTTCAGTAGCTATGGCATGCACTGGGTCCGCCAGGCTCCAGGCAAGGGGCTGGAGTGGGTGGCAGTTATATCATATGATGGAAGTAATAAATACTATGCAGACTCCGTGAAGGGCCGATTCACCATCTCCAGAGACAATTCCAAGAACACGCTGTATCTGCAAATGAACAGCCTGAGAGCTGAGGACACGGCTGTGTATTACTGTGCGAAAATTACTACTACTACTACGGTATGGACGTCTGGGGCCAAGGGACCACGGTCACCGTCTCCTCAGGGAGTGCATCCGCCCCAACCCTTTTCCCCCTCGTCTCCTGTGAGAATTCCCCGTCGGATACGAGCAGCGTG	TGTGCGAAGGGGAGAGTCGTAGTGGGAGCTACTACCCCCAGGAACTACTACTACTACGGTATGGACGTCTGG	CAKGRVVVGATTPRNYYYYGMDVW	72	24	81	431			463	525	526	596	1092	14	T

src/scirpy/tests/test_io.py

@@ -245,6 +245,7 @@ def test_write_airr_none_field_issue_454(tmp_path):
     write_airr(adata, tmp_path / "test.airr.tsv")
 
 
+@pytest.mark.xfail(reason="Dandelion still uses `duplicate_count` instead of `umi_count`", raises=AssertionError)
 @pytest.mark.extra
 @pytest.mark.parametrize(
     "anndata_from_10x_sample",
@@ -269,7 +270,7 @@ def test_convert_dandelion(anndata_from_10x_sample):
         assert len(ir_obj1.chains) == len(ir_obj2.chains)
 
         def _key(chain):
-            v1, v2 = chain.get("duplicate_count", -1), chain.get("junction", "")
+            v1, v2 = chain.get("umi_count", -1), chain.get("junction", "")
             v1 = -1 if v1 is None else v1
             v2 = "" if v2 is None else v2
             return (v1, v2)
@@ -295,7 +296,7 @@ def test_read_10x_csv():
             [
                 "junction_aa",
                 "junction",
-                "duplicate_count",
+                "umi_count",
                 "consensus_count",
                 "v_call",
                 "d_call",
@@ -314,7 +315,7 @@ def test_read_10x_csv():
     assert cell1.name == "AAACCTGAGTACGCCC-1"
     assert cell1["VDJ_1_junction_aa"] == "CASSLGPSTDTQYF"
     assert cell1["VDJ_1_junction"] == "TGTGCCAGCAGCTTGGGACCTAGCACAGATACGCAGTATTTT"
-    assert cell1["VDJ_1_duplicate_count"] == 55
+    assert cell1["VDJ_1_umi_count"] == 55
     assert cell1["VDJ_1_consensus_count"] == 18021
     assert cell1["VDJ_1_v_call"] == "TRBV7-2"
     assert cell1["VDJ_1_d_call"] == "TRBD2"
@@ -327,8 +328,8 @@ def test_read_10x_csv():
     assert cell2.name == "AAACCTGGTCCGTTAA-1"
     assert cell2["VJ_1_junction_aa"] == "CALNTGGFKTIF"
     assert cell2["VJ_2_junction_aa"] == "CAVILDARLMF"
-    assert cell2["VJ_1_duplicate_count"] == 5
-    assert cell2["VJ_2_duplicate_count"] == 5
+    assert cell2["VJ_1_umi_count"] == 5
+    assert cell2["VJ_2_umi_count"] == 5
     assert cell2["VJ_1_locus"] == "TRA"
     assert cell2["VDJ_1_locus"] == "TRB"
     assert cell2["VJ_2_locus"] == "TRA"
@@ -427,7 +428,7 @@ def test_read_10x():
                 "junction",
                 "np1_length",
                 "np2_length",
-                "duplicate_count",
+                "umi_count",
                 "consensus_count",
                 "v_call",
                 "d_call",
@@ -450,7 +451,7 @@ def test_read_10x():
     assert cell1["VDJ_1_junction"] == "TGTGCCAGCTCACCACCGAGCCAGGGCCTTTCTACCGGGGAGCTGTTTTTT"
     assert cell1["VDJ_1_np1_length"] == 4
     assert cell1["VDJ_1_np2_length"] == 7
-    assert cell1["VDJ_1_duplicate_count"] == 1
+    assert cell1["VDJ_1_umi_count"] == 1
     assert cell1["VDJ_1_consensus_count"] == 494
     assert cell1["VDJ_1_v_call"] == "TRBV18"
     assert cell1["VDJ_1_d_call"] == "TRBD1"
@@ -462,8 +463,8 @@ def test_read_10x():
     assert cell2.name == "AAACCTGAGTACGCCC-1"
     assert cell2["VJ_1_junction_aa"] == "CAMRVGGSQGNLIF"
     assert cell2["VJ_2_junction_aa"] == "CATDAKDSNYQLIW"
-    assert cell2["VJ_1_duplicate_count"] == 9
-    assert cell2["VJ_2_duplicate_count"] == 4
+    assert cell2["VJ_1_umi_count"] == 9
+    assert cell2["VJ_2_umi_count"] == 4
     assert np.all(_is_na(cell2[["VDJ_1_junction_aa", "VDJ_2_junction_aa"]]))
     assert cell2["VJ_1_np1_length"] == 4
     assert _is_na(cell2["VJ_1_np2_length"])
@@ -816,7 +817,7 @@ def test_airr_df():
             "cell_id",
             "c_call",
             "consensus_count",
-            "duplicate_count",
+            "umi_count",
         ],
     )
 
@@ -874,7 +875,7 @@ def test_read_bd_per_cell_chain():
         adata,
         [
             "locus",
-            "duplicate_count",
+            "umi_count",
             "consensus_count",
             "junction",
             "junction_aa",
@@ -892,7 +893,7 @@ def test_read_bd_per_cell_chain():
     cell85 = obs.loc["85", :]
 
     assert cell1["VJ_1_locus"] == "TRA"
-    assert cell1["VJ_1_duplicate_count"] == 1
+    assert cell1["VJ_1_umi_count"] == 1
     assert cell1["VJ_1_consensus_count"] == 72
     assert cell1["VJ_1_junction"] == "GCTGCCCCAGAATTTTGTC"
     assert cell1["VJ_1_junction_aa"] == "AAGQNFV"
@@ -918,11 +919,11 @@ def test_read_bd_per_cell_chain():
 def test_read_bd_contigs():
     adata = read_bd_rhapsody(TESTDATA / "bd/test_unfiltered_contigs.csv")
 
-    obs = ir.get.airr(adata, ["locus", "duplicate_count"], "VJ_1")
+    obs = ir.get.airr(adata, ["locus", "umi_count"], "VJ_1")
 
     assert obs.shape[0] == 5
 
     cell10681 = obs.loc["10681"]
 
     assert cell10681["VJ_1_locus"] == "IGK"
-    assert cell10681["VJ_1_duplicate_count"] == 2
+    assert cell10681["VJ_1_umi_count"] == 2