Quick start¶
This page provides details to start using the Census. Click here for more detailed Python tutorials (R vignettes coming soon).
Contents
Installation¶
Install the Census API by following these instructions.
Python quick start¶
Below are 3 examples of common operations you can do with the Census. As a reminder, the reference documentation for the API can be accessed via help():
import cellxgene_census
help(cellxgene_census)
help(cellxgene_census.get_anndata)
# etc
Querying a slice of cell metadata.¶
The following reads the cell metadata and filters female cells of cell type microglial cell or neuron, and selects the columns assay, cell_type, tissue, tissue_general, suspension_type, and disease.
import cellxgene_census
with cellxgene_census.open_soma() as census:
# Reads SOMADataFrame as a slice
cell_metadata = census["census_data"]["homo_sapiens"].obs.read(
value_filter = "sex == 'female' and cell_type in ['microglial cell', 'neuron']",
column_names = ["assay", "cell_type", "tissue", "tissue_general", "suspension_type", "disease"]
)
# Concatenates results to pyarrow.Table
cell_metadata = cell_metadata.concat()
# Converts to pandas.DataFrame
cell_metadata = cell_metadata.to_pandas()
print(cell_metadata)
The output is a pandas.DataFrame with about 300K cells meeting our query criteria and the selected columns.
assay cell_type tissue tissue_general suspension_type disease sex
0 10x 3' v3 microglial cell eye eye cell normal female
1 10x 3' v3 microglial cell eye eye cell normal female
2 10x 3' v3 microglial cell eye eye cell normal female
3 10x 3' v3 microglial cell eye eye cell normal female
4 10x 3' v3 microglial cell eye eye cell normal female
... ... ... ... ... ... ... ...
299617 10x 3' v3 neuron cerebral cortex brain nucleus normal female
299618 10x 3' v3 neuron cerebral cortex brain nucleus normal female
299619 10x 3' v3 neuron cerebral cortex brain nucleus normal female
299620 10x 3' v3 neuron cerebral cortex brain nucleus normal female
299621 10x 3' v3 neuron cerebral cortex brain nucleus normal female
[299622 rows x 7 columns]
Obtaining a slice as AnnData¶
The following creates an anndata.AnnData object on-demand with the same cell filtering criteria as above and filtering only the genes ENSG00000161798, ENSG00000188229.
import cellxgene_census
with cellxgene_census.open_soma() as census:
adata = cellxgene_census.get_anndata(
census = census,
organism = "Homo sapiens",
var_value_filter = "feature_id in ['ENSG00000161798', 'ENSG00000188229']",
obs_value_filter = "sex == 'female' and cell_type in ['microglial cell', 'neuron']",
column_names = {"obs": ["assay", "cell_type", "tissue", "tissue_general", "suspension_type", "disease"]},
)
print(adata)
The output with about 300K cells and 2 genes can be now used for downstream analysis using scanpy.
AnnData object with n_obs × n_vars = 299622 × 2
obs: 'assay', 'cell_type', 'tissue', 'tissue_general', 'suspension_type', 'disease', 'sex'
var: 'soma_joinid', 'feature_id', 'feature_name', 'feature_length'
Memory-efficient queries¶
This example provides a demonstration to access the data for larger-than-memory operations using TileDB-SOMA operations.
First we initiate a lazy-evaluation query to access all brain and male cells from human. This query needs to be closed — query.close() — or called in a context manager — with ....
import cellxgene_census
with cellxgene_census.open_soma() as census:
human = census["census_data"]["homo_sapiens"]
query = human.axis_query(
measurement_name = "RNA",
obs_query = tiledbsoma.AxisQuery(
value_filter = "tissue == 'brain' and sex == 'male'"
)
# Continued below
Now we can iterate over the matrix count, as well as the cell and gene metadata. For example, to iterate over the matrix count, we can get an iterator and perform operations for each iteration.
# Continued from above
iterator = query.X("raw").tables()
# Get an iterative slice as pyarrow.Table
raw_slice = next (iterator)
...
And you can now perform operations on each iteration slice. As with any any Python iterator this logic can be wrapped around a for loop.
And you must close the query.
# Continued from above
query.close()
R quick start¶
❗ API is in beta and under rapid development.
Below are 3 examples of common operations you can do with the Census. As a reminder, the reference documentation for the API can be accessed via ?:
library("cellxgene.census")
?cellxgene.census::get_seurat
Querying a slice of cell metadata.¶
The following reads the cell metadata and filters female cells of cell type microglial cell or neuron, and selects the columns assay, cell_type, tissue, tissue_general, suspension_type, and disease.
The cellxgene.census package uses R6 classes and we recommend you to get familiar with their usage.
library("cellxgene.census")
census = open_soma()
# Open obs SOMADataFrame
cell_metadata = census$get("census_data")$get("homo_sapiens")$get("obs")
# Read as Arrow Table
cell_metadata = cell_metadata$read(
value_filter = "sex == 'female' & cell_type %in% c('microglial cell', 'neuron')",
column_names = c("assay", "cell_type", "sex", "tissue", "tissue_general", "suspension_type", "disease")
)
# Convert to R tibble (dataframe)
cell_metadata = as.data.frame(cell_metadata)
print(cell_metadata)
The output is a tibble with about 300K cells meeting our query criteria and the selected columns.
# A tibble: 305,735 × 7
assay cell_type sex tissue tissue_general suspension_type disease
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 10x 3' v3 neuron female lung lung nucleus normal
2 10x 3' v3 neuron female lung lung nucleus normal
3 10x 3' v3 neuron female lung lung nucleus normal
4 10x 3' v3 neuron female lung lung nucleus normal
5 10x 3' v3 neuron female lung lung nucleus normal
6 10x 3' v3 neuron female lung lung nucleus normal
7 10x 3' v3 neuron female lung lung nucleus normal
8 10x 3' v3 neuron female lung lung nucleus normal
9 10x 3' v3 neuron female lung lung nucleus normal
10 10x 3' v3 neuron female lung lung nucleus normal
# ℹ 305,725 more rows
# ℹ Use `print(n = ...)` to see more rows
Obtaining a slice as a Seurat object¶
The following creates an Seurat object on-demand with the smaller set of cells and filtering only the genes ENSG00000161798, ENSG00000188229.
library("cellxgene.census")
census = open_soma()
seurat_obj = get_seurat(
census = census,
organism = "Homo sapiens",
var_value_filter = "feature_id %in% c('ENSG00000161798', 'ENSG00000188229')",
obs_value_filter = "sex == 'female' & cell_type %in% c('microglial cell', 'neuron')",
obs_column_names = c("assay", "cell_type", "tissue", "tissue_general", "suspension_type", "disease")
)
print(seurat_obj)
The output with about 5K cells and 2 genes can be now used for downstream analysis using Seurat.
An object of class Seurat
2 features across 5876 samples within 1 assay
Active assay: RNA (2 features, 0 variable features)
Memory-efficient queries¶
Memory-efficient capabilities of the R API are still under active development.