Represent single-cell experiments¶
This package provides container class to represent single-cell experimental data as 2-dimensional matrices. In these matrices, the rows typically denote features or genomic regions of interest, while columns represent cells. In addition, a SingleCellExperiment (SCE) object may contain low-dimensionality embeddings, alternative experiments performed on same sample or set of cells.
Important
The design of SingleCellExperiment class and its derivates adheres to the R/Bioconductor specification, where rows correspond to features, and columns represent cells.
Note
These classes follow a functional paradigm for accessing or setting properties, with further details discussed in functional paradigm section.
Installation¶
To get started, install the package from PyPI
pip install singlecellexperiment
Construction¶
The SingleCellExperiment extends RangeSummarizedExperiment and contains additional attributes:
reduced_dims: Slot for low-dimensionality embeddings for each cell.alternative_experiments: Manages multi-modal experiments performed on the same sample or set of cells.row_pairsorcolumn_pairs: Stores relationships between features or cells.
Note
In contrast to R, matrices in Python are unnamed and do not contain row or column names. Hence, these matrices cannot be directly used as values in assays or alternative experiments. We strictly enforce type checks in these cases. To relax these restrictions for alternative experiments, set type_check_alternative_experiments to False.
Important
If you are using the alternative_experiments slot, the number of cells must match the parent experiment. Otherwise, the expectation is that the cells do not share the same sample or annotations and cannot be set in alternative experiments!
Before we construct a SingleCellExperiment object, lets generate information about rows, columns and a mock experimental data from single-cell rna-seq experiments:
import pandas as pd
import numpy as np
from scipy import sparse as sp
from biocframe import BiocFrame
from genomicranges import GenomicRanges
from random import random
nrows = 200
ncols = 6
counts = sp.rand(nrows, ncols, density=0.2, format="csr")
row_data = BiocFrame(
{
"seqnames": [
"chr1",
"chr2",
"chr2",
"chr2",
"chr1",
"chr1",
"chr3",
"chr3",
"chr3",
"chr3",
]
* 20,
"starts": range(100, 300),
"ends": range(110, 310),
"strand": ["-", "+", "+", "*", "*", "+", "+", "+", "-", "-"] * 20,
"score": range(0, 200),
"GC": [random() for _ in range(10)] * 20,
}
)
col_data = pd.DataFrame(
{
"celltype": ["cluster1", "cluster2"] * 3,
}
)
Now lets create the SingleCellExperiment instance:
from singlecellexperiment import SingleCellExperiment
sce = SingleCellExperiment(
assays={"counts": counts}, row_data=row_data, column_data=col_data,
reduced_dims = {"random_embeds": np.random.rand(ncols, 4)}
)
print(sce)
class: SingleCellExperiment
dimensions: (200, 6)
assays(1): ['counts']
row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
row_names(0):
column_data columns(1): ['celltype']
column_names(6): ['0', '1', '2', '3', '4', '5']
main_experiment_name:
reduced_dims(1): ['random_embeds']
alternative_experiments(0): []
row_pairs(0): []
column_pairs(0): []
metadata(0):
Tip
You can also use delayed or file-backed arrays for representing experimental data, check out this section from summarized experiment.
Interop with anndata¶
We provide convenient methods for loading an AnnData or h5ad file into SingleCellExperiment objects.
For example, lets create an AnnData object,
import anndata as ad
from scipy import sparse as sp
counts = sp.csr_matrix(np.random.poisson(1, size=(100, 2000)), dtype=np.float32)
adata = ad.AnnData(counts)
print(adata)
AnnData object with n_obs × n_vars = 100 × 2000
Converting AnnData as SingleCellExperiment is straightforward:
sce_adata = SingleCellExperiment.from_anndata(adata)
print(sce_adata)
class: SingleCellExperiment
dimensions: (2000, 100)
assays(1): ['X']
row_data columns(0): []
row_names(2000): ['0', '1', '2', ..., '1997', '1998', '1999']
column_data columns(0): []
column_names(100): ['0', '1', '2', ..., '97', '98', '99']
main_experiment_name:
reduced_dims(0): []
alternative_experiments(0): []
row_pairs(0): []
column_pairs(0): []
metadata(1): uns
and vice-verse. All assays from SCE are represented in the layers slot of the AnnData object:
adata2 = sce_adata.to_anndata()
print(adata2)
(AnnData object with n_obs × n_vars = 100 × 2000
uns: 'uns'
layers: 'X', None)
/home/runner/work/SingleCellExperiment/SingleCellExperiment/.tox/docs/lib/python3.9/site-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.
warnings.warn("Transforming to str index.", ImplicitModificationWarning)
Similarly, one can load a h5ad file:
from singlecellexperiment import read_h5ad
sce_h5 = read_h5ad("../../assets/data/adata.h5ad")
print(sce_h5)
From 10X formats¶
In addition, we also provide convenient methods to load a 10X Genomics HDF5 Feature-Barcode Matrix Format file.
from singlecellexperiment import read_tenx_h5
sce_h5 = read_tenx_h5("../../assets/data/tenx.sub.h5")
print(sce_h5)
Note
Methods are also available to read a 10x matrix market directory using the read_tenx_mtx function.
Getters/Setters¶
Getters are available to access various attributes using either the property notation or functional style.
Since SingleCellExperiment extends RangedSummarizedExperiment, all getters and setters from the base class are accessible here; more details here.
# access assay names
print("reduced dim names (as property): ", sce.reduced_dim_names)
print("reduced dim names (functional style): ", sce.get_reduced_dim_names())
# access row data
print(sce.row_data)
reduced dim names (as property): ['random_embeds']
reduced dim names (functional style): ['random_embeds']
BiocFrame with 200 rows and 6 columns
seqnames starts ends strand score GC
<list> <range> <range> <list> <range> <list>
[0] chr1 100 110 - 0 0.23929243158312297
[1] chr2 101 111 + 1 0.42055100724885097
[2] chr2 102 112 + 2 0.5580973577803723
... ... ... ... ... ...
[197] chr3 297 307 + 197 0.9039167253656578
[198] chr3 298 308 - 198 0.17491260994572377
[199] chr3 299 309 - 199 0.9046480123365814
Access a reduced dimension¶
One can access an reduced dimension by index or name:
sce.reduced_dim(0) # same as se.reduced_dim("random_embeds")
array([[0.65444999, 0.90601852, 0.87720018, 0.09727131],
[0.27040466, 0.05925716, 0.40215101, 0.35083125],
[0.3231138 , 0.73572795, 0.98296675, 0.92569003],
[0.33351471, 0.57698531, 0.26307458, 0.06270545],
[0.51755486, 0.74829941, 0.09911028, 0.50273699],
[0.76457642, 0.9932149 , 0.4097655 , 0.1936202 ]])
Subset experiments¶
You can subset experimental data by using the subset ([]) operator. This operation accepts different slice input types, such as a boolean vector, a slice object, a list of indices, or names (if available) to subset.
In our previous example, we didn’t include row or column names. Let’s create another SingleCellExperiment object that includes names.
subset_sce = sce[0:10, 0:3]
print(subset_sce)
class: SingleCellExperiment
dimensions: (10, 3)
assays(1): ['counts']
row_data columns(6): ['seqnames', 'starts', 'ends', 'strand', 'score', 'GC']
row_names(0):
column_data columns(1): ['celltype']
column_names(3): ['0', '1', '2']
main_experiment_name:
reduced_dims(1): ['random_embeds']
alternative_experiments(0): []
row_pairs(0): []
column_pairs(0): []
metadata(0):
Combining experiments¶
SingleCellExperiment implements methods for the combine generic from BiocUtils.
These methods enable the merging or combining of multiple SingleCellExperiment objects, allowing users to aggregate data from different experiments or conditions. Note: row_pairs and column_pairs are not ignored as part of this operation.
To demonstrate, let’s create multiple SingleCellExperiment objects (read more about this in combine section from SummarizedExperiment).
ncols = 10
nrows = 100
sce1 = SingleCellExperiment(
assays={"counts": np.random.poisson(lam=10, size=(nrows, ncols))},
row_data=BiocFrame({"A": [1] * nrows}),
column_data=BiocFrame({"A": [1] * ncols}),
)
sce2 = SingleCellExperiment(
assays={
"counts": np.random.poisson(lam=10, size=(nrows, ncols)),
# "normalized": np.random.normal(size=(nrows, ncols)),
},
row_data=BiocFrame({"A": [3] * nrows}),
column_data=BiocFrame({"A": [3] * ncols}),
)
rowdata1 = pd.DataFrame(
{
"seqnames": ["chr_5", "chr_3", "chr_2"],
"start": [500, 300, 200],
"end": [510, 310, 210],
},
index=["HER2", "BRCA1", "TPFK"],
)
coldata1 = pd.DataFrame(
{
"sample": ["SAM_1", "SAM_2", "SAM_3"],
"disease": ["True", "True", "True"],
"doublet_score": [0.15, 0.62, 0.18],
},
index=["cell_1", "cell_2", "cell_3"],
)
sce_alts1 = SingleCellExperiment(
assays={
"counts": np.random.poisson(lam=5, size=(3, 3)),
"lognorm": np.random.lognormal(size=(3, 3)),
},
row_data=rowdata1,
column_data=coldata1,
row_names=["HER2", "BRCA1", "TPFK"],
column_names=["cell_1", "cell_2", "cell_3"],
metadata={"seq_type": "paired"},
reduced_dims={"PCA": np.random.poisson(lam=10, size=(3, 5))},
alternative_experiments={
"modality1": SingleCellExperiment(
assays={"counts2": np.random.poisson(lam=10, size=(3, 3))},
)
},
)
rowdata2 = pd.DataFrame(
{
"seqnames": ["chr_5", "chr_3", "chr_2"],
"start": [500, 300, 200],
"end": [510, 310, 210],
},
index=["HER2", "BRCA1", "TPFK"],
)
coldata2 = pd.DataFrame(
{
"sample": ["SAM_4", "SAM_5", "SAM_6"],
"disease": ["True", "False", "True"],
"doublet_score": [0.05, 0.23, 0.54],
},
index=["cell_4", "cell_5", "cell_6"],
)
sce_alts2 = SingleCellExperiment(
assays={
"counts": np.random.poisson(lam=5, size=(3, 3)),
# "lognorm": np.random.lognormal(size=(3, 3)),
},
row_data=rowdata2,
column_data=coldata2,
metadata={"seq_platform": "Illumina NovaSeq 6000"},
reduced_dims={"PCA": np.random.poisson(lam=5, size=(3, 5))},
alternative_experiments={
"modality1": SingleCellExperiment(
assays={"counts2": np.random.poisson(lam=5, size=(3, 3))},
)
},
)
The combine_rows or combine_columns operations, expect all experiments to contain the same assay names. To combine experiments by row:
from biocutils import relaxed_combine_columns, combine_columns, combine_rows, relaxed_combine_rows
sce_combined = combine_rows(sce2, sce1)
print(sce_combined)
class: SingleCellExperiment
dimensions: (200, 10)
assays(1): ['counts']
row_data columns(1): ['A']
row_names(0):
column_data columns(1): ['A']
column_names(0):
main_experiment_name:
reduced_dims(0): []
alternative_experiments(0): []
row_pairs(0): []
column_pairs(0): []
metadata(0):
/home/runner/work/SingleCellExperiment/SingleCellExperiment/.tox/docs/lib/python3.9/site-packages/singlecellexperiment/SingleCellExperiment.py:1092: UserWarning: 'row_pairs' and 'column_pairs' are currently ignored during this operation.
warn(
Similarly to combine by column:
sce_combined = combine_columns(sce2, sce1)
print(sce_combined)
class: SingleCellExperiment
dimensions: (100, 20)
assays(1): ['counts']
row_data columns(1): ['A']
row_names(0):
column_data columns(1): ['A']
column_names(0):
main_experiment_name:
reduced_dims(0): []
alternative_experiments(0): []
row_pairs(0): []
column_pairs(0): []
metadata(0):
/home/runner/work/SingleCellExperiment/SingleCellExperiment/.tox/docs/lib/python3.9/site-packages/singlecellexperiment/SingleCellExperiment.py:1134: UserWarning: 'row_pairs' and 'column_pairs' are currently ignored during this operation.
warn(
Note
You can use relaxed_combine_columns or relaxed_combined_rows when there’s mismatch in the number of features or samples. Missing rows or columns in any object are filled in with appropriate placeholder values before combining, e.g. missing assay’s are replaced with a masked numpy array.
# sce_alts1 contains an additional assay not present in sce_alts2
sce_relaxed_combine = relaxed_combine_columns(sce_alts1, sce_alts2)
print(sce_relaxed_combine)
class: SingleCellExperiment
dimensions: (3, 6)
assays(2): ['lognorm', 'counts']
row_data columns(3): ['seqnames', 'start', 'end']
row_names(3): ['HER2', 'BRCA1', 'TPFK']
column_data columns(3): ['sample', 'disease', 'doublet_score']
column_names(6): ['cell_1', 'cell_2', 'cell_3', 'cell_4', 'cell_5', 'cell_6']
main_experiment_name:
reduced_dims(1): ['PCA']
alternative_experiments(1): ['modality1']
row_pairs(0): []
column_pairs(0): []
metadata(1): seq_type
/home/runner/work/SingleCellExperiment/SingleCellExperiment/.tox/docs/lib/python3.9/site-packages/singlecellexperiment/SingleCellExperiment.py:1244: UserWarning: 'row_pairs' and 'column_pairs' are currently ignored during this operation.
warn("'row_pairs' and 'column_pairs' are currently ignored during this operation.")
Export as AnnData or MuData¶
The package also provides methods to convert a SingleCellExperiment object into a MuData representation:
mdata = sce.to_mudata()
mdata
/home/runner/work/SingleCellExperiment/SingleCellExperiment/.tox/docs/lib/python3.9/site-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.
warnings.warn("Transforming to str index.", ImplicitModificationWarning)
MuData object with n_obs × n_vars = 6 × 200
var: 'seqnames', 'starts', 'ends', 'strand', 'score', 'GC'
1 modality
Unknown Modality: 6 x 200
obs: 'celltype', 'rownames'
var: 'seqnames', 'starts', 'ends', 'strand', 'score', 'GC'
obsm: 'random_embeds'
layers: 'counts'or coerce to an AnnData object:
adata, alts = sce.to_anndata()
print("main experiment: ", adata)
print("alternative experiments: ", alts)
main experiment: AnnData object with n_obs × n_vars = 6 × 200
obs: 'celltype', 'rownames'
var: 'seqnames', 'starts', 'ends', 'strand', 'score', 'GC'
obsm: 'random_embeds'
layers: 'counts'
alternative experiments: None
/home/runner/work/SingleCellExperiment/SingleCellExperiment/.tox/docs/lib/python3.9/site-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.
warnings.warn("Transforming to str index.", ImplicitModificationWarning)