Doublet detection

36. Doublet detection#

36.1. Motivation#

In the Quality Control chapter, we removed cells that potentially reflect doublets based only on their high count content. We also filtered cells based on sample-wise distribution. Now, we will focus on heterotypic doublets, that is, doublets that contain cells from different cell types. With ADT data, we can detect them using cell type specific surface markers[Sun et al., 2021].

36.2. Environment setup#

import warnings

import muon as mu
import scanpy as sc

warnings.filterwarnings("ignore")
mu.set_options(pull_on_update=False)
sc.settings.verbosity = 0
sc.set_figure_params(
    dpi=80,
    facecolor="white",
    frameon=False,
)

import lamindb as ln

ln.track()

→ loaded Transform('xo1WtNUmrJCK0003', key='doublet_detection.ipynb'), re-started Run('hDXP6BIj1dnyLVwy') at 2026-04-10 17:27:34 UTC
→ notebook imports: lamindb-core==2.3.1 muon==0.1.7 scanpy==1.12
• recommendation: to identify the notebook across renames, pass the uid: ln.track("xo1WtNUmrJCK")

36.3. Loading the data#

We load the MuData object we saved at the end of the previous chapter Normalization:

af = ln.Artifact.connect("theislab/sc-best-practices").get(
    key="surface-protein/cite_normalization.h5mu", is_latest=True
)
mdata = af.load()
mdata

MuData object with n_obs × n_vars = 118563 × 36741
  var:	'gene_ids', 'feature_types'
  2 modalities
    rna:	118563 x 36601
      obs:	'donor', 'batch'
      var:	'gene_ids', 'feature_types'
    prot:	118563 x 140
      obs:	'donor', 'batch', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'n_counts', 'outliers'
      var:	'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'
      layers:	'counts'

36.4. Doublets detected with cell type markers#

We are now going to look at cell type markers that are mutually exclusive. Some examples are CD3 (T cell marker) vs CD19 (B cell marker) to identify T/B cells doublets. As cells expressing both specific B and T cell markers do not exist under physiological conditions, those droplets contain T/B cell doublets.

The same is true for cells expressing both T cell (CD3) and monocyte (CD14) markers.

sc.pl.scatter(mdata["prot"], x="CD3", y="CD19-1", color="log1p_total_counts")

../_images/0b20b5b11c155b346cc0bda48dc4134e69cd6fb34e947f82c9d840ee0659b277.png

In this plot, we can see a large number of cells not expressing T or B cell markers in the lower left, cells expressing only one marker in the upper left and lower right as well as some cells expressing both markers (upper right).

The cells expressing both markers are doublets and can be removed.

We can also use CD3 and CD14 to detect T/monocyte doublets.

sc.pl.scatter(mdata["prot"], x="CD3", y="CD14-1", color="log1p_total_counts")

../_images/44f95c2f1f6674cb644881fc0415cf1116794492ec31cb11e017686ba04ef76d.png

It looks like cells that have an expression level above 2.5 in both markers are doublets. We use an expression level above 2.5 in both markers to flag doublets.

genes2filter = ["CD3", "CD19-1", "CD14-1"]
temp = mdata["prot"][:, genes2filter].X.T.tolist()

mdata["prot"].obs["doublets_markers"] = [
    (temp[0][i] > 2.5 and temp[1][i] > 2.5) or (temp[0][i] > 2.5 and temp[2][i] > 2.5)
    for i in range(mdata.shape[0])
]
mdata["prot"].obs["doublets_markers"] = (
    mdata["prot"].obs["doublets_markers"].astype(str)
)

Doublets usually have a higher count due to the presence of increased counts from more than one cell. We can see this effect in the cells classified as doublets using our markers:

sc.pl.violin(mdata["prot"], keys="log1p_total_counts", groupby="doublets_markers")

../_images/a84b375f620e42850a66b7b15fe819feeef3bc70c077b228f4741a6fe80f4ba1.png

We leave out cells expressing both markers.

mdata = mdata[mdata["prot"].obs["doublets_markers"] == "False"].copy()
mdata

MuData object with n_obs × n_vars = 117951 × 36741
  var:	'gene_ids', 'feature_types'
  2 modalities
    rna:	117951 x 36601
      obs:	'donor', 'batch'
      var:	'gene_ids', 'feature_types'
    prot:	117951 x 140
      obs:	'donor', 'batch', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'n_counts', 'outliers', 'doublets_markers'
      var:	'gene_ids', 'feature_types', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'
      uns:	'doublets_markers_colors'
      layers:	'counts'

We removed 612 doublets from the data.

In this chapter, we removed doublets using the ADT data by removing cells that highly expressed two mutually exclusive markers. Another option to remove doublets would be utilizing methods that detect doublets based on the scRNA-seq data. For those methods, we refer to the Doublet Detection chapter of scRNA-seq preprocessing and visualization.

af_doublet_detection = ln.Artifact.from_mudata(
    mdata,
    key="surface-protein/cite_doublet_detection.h5mu",
    description="CITE-seq data after doublet detection",
)
af_doublet_detection.save()

ln.finish()

36.5. References#

[SBEO+21]

Bo Sun, Emmanuel Bugarin-Estrada, Lauren Elizabeth Overend, Catherine Elizabeth Walker, Felicia Anna Tucci, and Rachael Jennifer Mary Bashford-Rogers. Double-jeopardy: scrna-seq doublet/multiplet detection using multi-omic profiling. Cell Reports Methods, 1(1):100008, May 2021. doi:10.1016/j.crmeth.2021.100008.

36.6. Contributors#

We gratefully acknowledge the contributions of:

36.6.1. Authors#

Javier Marchena-Hurtado
Daniel Strobl
Ciro Ramírez-Suástegui

36.6.2. Reviewers#

Lukas Heumos
Anna Schaar