Census data and schema

This page provides a user-friendly overview of the Census contents and its schema, in case you are interested you can find the full schema specification here.

Contents

  1. Schema

  2. Data included in the Census

  3. SOMA objects

Schema

The Census is a collection of a variety of SOMA objects organized with the following hierarchy.

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As you can see the Census data is a SOMACollection with two high-level items:

  1. "census_info" for the census summary info.

  2. "census_data" for the single-cell data and metadata.

Census summary info "census_info"

A SOMAcollection with tables providing information of the census as a whole, it has the following items:

  • "summary": high-level information of this Census, e.g. build date, total cell count, etc.

  • "datasets": A table with all datasets from CELLxGENE Discover used to create the Census. -"summary_cell_counts": Cell counts stratified by relevant cell metadata

Census single-cell data "census_data"

Data for each organism is stored in independent SOMAExperiment objects which are a specialized form of a SOMACollection. Each of these store a data matrix (cell by genes), cell metadata, gene metadata, and feature presence matrix:

This is how the data is organized for one organism – Homo sapiens:

  • ["homo_sapiens"].obs: Cell metadata

  • ["homo_sapiens"].ms["RNA"].X: Data matrices, currently only raw counts exist X["raw"]

  • ["homo_sapiens"].ms["RNA"].var: Gene Metadata

  • ["homo_sapiens"].ms["RNA"]["feature_dataset_presence_matrix"]: a sparse boolean array indicating which genes were measured in each dataset.

Data included in the Census

All data from CZ CELLxGENE Discover that adheres to the following criteria is included in the Census:

  • Cells from human or mouse.

  • Non-spatial RNA data, see full list of sequencing technologies included here.

  • Raw counts.

  • Only standardized cell and gene metadata as described in the CELLxGENE Discover dataset schema.

SOMA objects

You can find the full SOMA specification here.

The following is short description of the main SOMA objects used by the Census:

  • DenseNDArray is a dense, N-dimensional array, with offset (zero-based) integer indexing on each dimension.

  • SparseNDArray is the same as DenseNDArray but sparse, and supports point indexing (disjoint index access).

  • DataFrame is a multi-column table with a user-defined columns names and value types, with support for point indexing.

  • Collection is a persistent container of named SOMA objects.

  • Experiment is a class that represents a single-cell experiment. It always contains two objects:

    • obs: a DataFrame with primary annotations on the observation axis.

    • ms: a Collection of measurements, each composed of X matrices and axis annotation matrices or data frames (e.g. var, varm, obsm, etc).