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Vignette#

Example workflow to convert CITESeq data to FCS file format.

This example used to 5k PBMC NextGEM dataset from 10X.

Download data from: http://cf.10xgenomics.com/samples/cell-exp/3.1.0/5k_pbmc_protein_v3_nextgem/5k_pbmc_protein_v3_nextgem_filtered_feature_bc_matrix.h5

[1]:

import os

import scanpy as sc

from anndata_fcs import anndata_to_fcs, fcs_to_anndata, fcs_to_dataframe, gate_polygon, scatter, gate_polygon_subset

Load anndata object from rawdata#

[2]:

adata = sc.read_10x_h5(
    filename=os.path.join(os.getcwd(), "..", "..", "data", "5k_pbmc_protein_v3_nextgem_filtered_feature_bc_matrix.h5"),
    gex_only=False,
)
adata.var_names_make_unique()

/home/malte/Dokumente/Github/citeseq_to_fcs/venv/lib/python3.10/site-packages/anndata/_core/anndata.py:1758: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.
  utils.warn_names_duplicates("var")

Filter CITESeq data#

[3]:

adata_citeseq = adata[:, adata.var["feature_types"] == "Antibody Capture"]
adata_citeseq

[3]:

View of AnnData object with n_obs × n_vars = 5527 × 32
    var: 'gene_ids', 'feature_types', 'genome', 'pattern', 'read', 'sequence'

Generate FCS file#

[4]:

# Convert data
fcs_data = anndata_to_fcs(adata_citeseq)

[5]:

adata_citeseq.obs.head()

[5]:

	barcode_rank
AAACCCACAGGCTTGC-1	0
AAACCCAGTAGTTAGA-1	1
AAACGAAGTAACGATA-1	2
AAACGAAGTGGATCAG-1	3
AAACGAATCATGAGAA-1	4

[6]:

# Save fcs file
fcs_data.write_fcs(os.path.join(os.getcwd(), "..", "..", "data", "citeseq.fcs"))

[7]:

fcs_df = fcs_to_dataframe(fcs_data)
fcs_df.head()

[7]:

	CD3_TotalSeqB	CD4_TotalSeqB	CD8a_TotalSeqB	CD11b_TotalSeqB	CD14_TotalSeqB	CD15_TotalSeqB	CD16_TotalSeqB	CD19_TotalSeqB	CD20_TotalSeqB	CD25_TotalSeqB	...	CD274_TotalSeqB	CD278_TotalSeqB	CD335_TotalSeqB	PD-1_TotalSeqB	HLA-DR_TotalSeqB	TIGIT_TotalSeqB	IgG1_control_TotalSeqB	IgG2a_control_TotalSeqB	IgG2b_control_TotalSeqB	barcode_rank
0	12.0	280.0	9.0	3122.0	746.0	9.0	1.0	5.0	2.0	4.0	...	2.0	6.0	8.0	4.0	101.0	4.0	5.0	2.0	4.0	0.0
1	24.0	231.0	11.0	1241.0	355.0	10.0	2.0	4.0	11.0	5.0	...	1.0	4.0	8.0	8.0	1450.0	5.0	0.0	1.0	1.0	1.0
2	23.0	117.0	3.0	582.0	133.0	7.0	4.0	1.0	7.0	3.0	...	4.0	5.0	2.0	2.0	524.0	2.0	1.0	4.0	2.0	2.0
3	8.0	83.0	1.0	1966.0	675.0	7.0	2.0	3.0	8.0	3.0	...	3.0	2.0	2.0	5.0	216.0	0.0	5.0	1.0	3.0	3.0
4	726.0	1100.0	5.0	14.0	13.0	6.0	2.0	7.0	6.0	11.0	...	2.0	129.0	4.0	5.0	9.0	5.0	1.0	3.0	4.0	4.0

5 rows × 33 columns

Gating on FCS file#

[8]:

in_polygon = gate_polygon(
    fcs_data,
    x="CD3_TotalSeqB",
    y="CD4_TotalSeqB",
    polygon=[[90, 200], [5000, 200], [5000, 5000], [90, 5000]],
)

[9]:

ax = scatter(
    data=fcs_df,
    x="CD3_TotalSeqB",
    y="CD4_TotalSeqB",
    gates={"CD3+ CD4+": [[90, 200], [5000, 200], [5000, 5000], [90, 5000]]},
    highlight=in_polygon,
    highlight_color="orange",
    color="gray",
)

_images/vignette_13_0.png

[10]:

fcs_to_dataframe(
    gate_polygon_subset(
        fcs_data,
        x="CD3_TotalSeqB",
        y="CD4_TotalSeqB",
        polygon=[[90, 200], [5000, 200], [5000, 5000], [90, 5000]],
    )
).head()

[10]:

	CD3_TotalSeqB	CD4_TotalSeqB	CD8a_TotalSeqB	CD11b_TotalSeqB	CD14_TotalSeqB	CD15_TotalSeqB	CD16_TotalSeqB	CD19_TotalSeqB	CD20_TotalSeqB	CD25_TotalSeqB	...	CD274_TotalSeqB	CD278_TotalSeqB	CD335_TotalSeqB	PD-1_TotalSeqB	HLA-DR_TotalSeqB	TIGIT_TotalSeqB	IgG1_control_TotalSeqB	IgG2a_control_TotalSeqB	IgG2b_control_TotalSeqB	barcode_rank
0	726.0	1100.0	5.0	14.0	13.0	6.0	2.0	7.0	6.0	11.0	...	2.0	129.0	4.0	5.0	9.0	5.0	1.0	3.0	4.0	4.0
1	342.0	1086.0	2.0	9.0	10.0	4.0	1.0	3.0	7.0	11.0	...	5.0	44.0	3.0	5.0	14.0	12.0	0.0	2.0	4.0	5.0
2	125.0	1144.0	1.0	26.0	8.0	8.0	2.0	2.0	5.0	17.0	...	4.0	36.0	0.0	4.0	11.0	2.0	3.0	2.0	1.0	7.0
3	538.0	1065.0	3.0	11.0	15.0	5.0	3.0	2.0	6.0	2.0	...	3.0	44.0	2.0	12.0	9.0	0.0	3.0	0.0	1.0	9.0
4	224.0	531.0	1.0	11.0	4.0	2.0	2.0	2.0	1.0	12.0	...	0.0	14.0	1.0	2.0	9.0	2.0	1.0	1.0	4.0	10.0

5 rows × 33 columns

[11]:

ax = scatter(
    data=fcs_to_dataframe(
        gate_polygon_subset(
            fcs_data,
            x="CD3_TotalSeqB",
            y="CD4_TotalSeqB",
            polygon=[[90, 200], [5000, 200], [5000, 5000], [90, 5000]],
        )
    ),
    x="CD3_TotalSeqB",
    y="CD4_TotalSeqB",
)

_images/vignette_15_0.png

Recover anndata from gated FCS file#

[12]:

adata_output = fcs_to_anndata(
    gate_polygon_subset(
        fcs_data,
        x="CD3_TotalSeqB",
        y="CD4_TotalSeqB",
        polygon=[[90, 200], [5000, 200], [5000, 5000], [90, 5000]],
    ),
    include_metadata=False,
)
adata_output

[12]:

AnnData object with n_obs × n_vars = 2581 × 33

[13]:

adata_citeseq[adata_citeseq.obs["barcode_rank"].isin(adata_output[:, ["barcode_rank"]].X.flatten()), :]

[13]:

View of AnnData object with n_obs × n_vars = 2581 × 32
    obs: 'barcode_rank'
    var: 'gene_ids', 'feature_types', 'genome', 'pattern', 'read', 'sequence'