cellxgene-census

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CZ CELLxGENE Census

Overview

概述

The CZ CELLxGENE Census provides programmatic access to a comprehensive, versioned collection of standardized single-cell genomics data from CZ CELLxGENE Discover. This skill enables efficient querying and analysis of millions of cells across thousands of datasets.

The Census includes:

61+ million cells from human and mouse
Standardized metadata (cell types, tissues, diseases, donors)
Raw gene expression matrices
Pre-calculated embeddings and statistics
Integration with PyTorch, scanpy, and other analysis tools

CZ CELLxGENE Census 提供对CZ CELLxGENE Discover中标准化单细胞基因组数据的全面、版本化集合的程序化访问。本技能支持对跨数千个数据集的数百万细胞进行高效查询和分析。

Census包含：

6100多万个人类和小鼠细胞
标准化元数据（细胞类型、组织、疾病、供体）
原始基因表达矩阵
预计算嵌入和统计数据
与PyTorch、scanpy及其他分析工具集成

When to Use This Skill

何时使用本技能

This skill should be used when:

Querying single-cell expression data by cell type, tissue, or disease
Exploring available single-cell datasets and metadata
Training machine learning models on single-cell data
Performing large-scale cross-dataset analyses
Integrating Census data with scanpy or other analysis frameworks
Computing statistics across millions of cells
Accessing pre-calculated embeddings or model predictions

在以下场景中应使用本技能：

按细胞类型、组织或疾病查询单细胞表达数据
探索可用的单细胞数据集和元数据
在单细胞数据上训练机器学习模型
执行大规模跨数据集分析
将Census数据与scanpy或其他分析框架集成
计算数百万细胞的统计数据
访问预计算嵌入或模型预测结果

Installation and Setup

安装与设置

Install the Census API:

bash

uv pip install cellxgene-census

For machine learning workflows, install additional dependencies:

bash

uv pip install cellxgene-census[experimental]

安装Census API：

bash

uv pip install cellxgene-census

对于机器学习工作流，安装额外依赖：

bash

uv pip install cellxgene-census[experimental]

Core Workflow Patterns

核心工作流模式

1. Opening the Census

1. 打开Census

Always use the context manager to ensure proper resource cleanup:

python

import cellxgene_census

始终使用上下文管理器以确保资源正确清理：

python

import cellxgene_census

Open latest stable version

打开最新稳定版本

with cellxgene_census.open_soma() as census: # Work with census data

with cellxgene_census.open_soma() as census: # 处理census数据

Open specific version for reproducibility

打开特定版本以保证可复现性

with cellxgene_census.open_soma(census_version="2023-07-25") as census: # Work with census data


**Key points:**
- Use context manager (`with` statement) for automatic cleanup
- Specify `census_version` for reproducible analyses
- Default opens latest "stable" release

with cellxgene_census.open_soma(census_version="2023-07-25") as census: # 处理census数据


**关键点：**
- 使用上下文管理器（`with`语句）自动清理资源
- 指定`census_version`以实现可复现分析
- 默认打开最新的"stable"版本

2. Exploring Census Information

2. 探索Census信息

Before querying expression data, explore available datasets and metadata.

Access summary information:

python

undefined

在查询表达数据之前，先探索可用的数据集和元数据。

访问摘要信息：

python

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Get summary statistics

获取统计摘要

summary = census["census_info"]["summary"].read().concat().to_pandas() print(f"Total cells: {summary['total_cell_count'][0]}")

summary = census["census_info"]["summary"].read().concat().to_pandas() print(f"总细胞数: {summary['total_cell_count'][0]}")

Get all datasets

获取所有数据集

datasets = census["census_info"]["datasets"].read().concat().to_pandas()

Filter datasets by criteria

按条件筛选数据集

covid_datasets = datasets[datasets["disease"].str.contains("COVID", na=False)]


**Query cell metadata to understand available data:**
```python

covid_datasets = datasets[datasets["disease"].str.contains("COVID", na=False)]


**查询细胞元数据以了解可用数据：**
```python

Get unique cell types in a tissue

获取某一组织中的独特细胞类型

cell_metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="tissue_general == 'brain' and is_primary_data == True", column_names=["cell_type"] ) unique_cell_types = cell_metadata["cell_type"].unique() print(f"Found {len(unique_cell_types)} cell types in brain")

cell_metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="tissue_general == 'brain' and is_primary_data == True", column_names=["cell_type"] ) unique_cell_types = cell_metadata["cell_type"].unique() print(f"在大脑中发现{len(unique_cell_types)}种细胞类型")

Count cells by tissue

按组织统计细胞数量

tissue_counts = cell_metadata.groupby("tissue_general").size()


**Important:** Always filter for `is_primary_data == True` to avoid counting duplicate cells unless specifically analyzing duplicates.

tissue_counts = cell_metadata.groupby("tissue_general").size()


**重要提示：** 除非专门分析重复细胞，否则始终筛选`is_primary_data == True`以避免统计重复细胞。

3. Querying Expression Data (Small to Medium Scale)

3. 查询表达数据（中小规模）

For queries returning < 100k cells that fit in memory, use

get_anndata()

python

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对于返回细胞数<10万且可放入内存的查询，使用

get_anndata()

：

python

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Basic query with cell type and tissue filters

带细胞类型和组织筛选的基础查询

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", # or "Mus musculus" obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True", obs_column_names=["assay", "disease", "sex", "donor_id"], )

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", # 或 "Mus musculus" obs_value_filter="cell_type == 'B cell' and tissue_general == 'lung' and is_primary_data == True", obs_column_names=["assay", "disease", "sex", "donor_id"], )

Query specific genes with multiple filters

带多条件筛选的特定基因查询

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19', 'FOXP3']", obs_value_filter="cell_type == 'T cell' and disease == 'COVID-19' and is_primary_data == True", obs_column_names=["cell_type", "tissue_general", "donor_id"], )


**Filter syntax:**
- Use `obs_value_filter` for cell filtering
- Use `var_value_filter` for gene filtering
- Combine conditions with `and`, `or`
- Use `in` for multiple values: `tissue in ['lung', 'liver']`
- Select only needed columns with `obs_column_names`

**Getting metadata separately:**
```python


**筛选语法：**
- 使用`obs_value_filter`进行细胞筛选
- 使用`var_value_filter`进行基因筛选
- 使用`and`、`or`组合条件
- 使用`in`匹配多个值：`tissue in ['lung', 'liver']`
- 使用`obs_column_names`仅选择所需列

**单独获取元数据：**
```python

Query cell metadata

查询细胞元数据

cell_metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="disease == 'COVID-19' and is_primary_data == True", column_names=["cell_type", "tissue_general", "donor_id"] )

Query gene metadata

查询基因元数据

gene_metadata = cellxgene_census.get_var( census, "homo_sapiens", value_filter="feature_name in ['CD4', 'CD8A']", column_names=["feature_id", "feature_name", "feature_length"] )

undefined

gene_metadata = cellxgene_census.get_var( census, "homo_sapiens", value_filter="feature_name in ['CD4', 'CD8A']", column_names=["feature_id", "feature_name", "feature_length"] )

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4. Large-Scale Queries (Out-of-Core Processing)

4. 大规模查询（核外处理）

For queries exceeding available RAM, use

axis_query()

with iterative processing:

python

import tiledbsoma as soma

对于超出可用内存的查询，使用

axis_query()

进行迭代处理：

python

import tiledbsoma as soma

Create axis query

创建轴查询

query = census["census_data"]["homo_sapiens"].axis_query( measurement_name="RNA", obs_query=soma.AxisQuery( value_filter="tissue_general == 'brain' and is_primary_data == True" ), var_query=soma.AxisQuery( value_filter="feature_name in ['FOXP2', 'TBR1', 'SATB2']" ) )

Iterate through expression matrix in chunks

分块迭代处理表达矩阵

iterator = query.X("raw").tables() for batch in iterator: # batch is a pyarrow.Table with columns: # - soma_data: expression value # - soma_dim_0: cell (obs) coordinate # - soma_dim_1: gene (var) coordinate process_batch(batch)


**Computing incremental statistics:**
```python

iterator = query.X("raw").tables() for batch in iterator: # batch是一个pyarrow.Table，包含以下列： # - soma_data: 表达值 # - soma_dim_0: 细胞（obs）坐标 # - soma_dim_1: 基因（var）坐标 process_batch(batch)


**计算增量统计数据：**
```python

Example: Calculate mean expression

示例：计算平均表达量

n_observations = 0 sum_values = 0.0

iterator = query.X("raw").tables() for batch in iterator: values = batch["soma_data"].to_numpy() n_observations += len(values) sum_values += values.sum()

mean_expression = sum_values / n_observations

undefined

n_observations = 0 sum_values = 0.0

iterator = query.X("raw").tables() for batch in iterator: values = batch["soma_data"].to_numpy() n_observations += len(values) sum_values += values.sum()

mean_expression = sum_values / n_observations

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5. Machine Learning with PyTorch

5. 与PyTorch结合的机器学习

For training models, use the experimental PyTorch integration:

python

from cellxgene_census.experimental.ml import experiment_dataloader

with cellxgene_census.open_soma() as census:
    # Create dataloader
    dataloader = experiment_dataloader(
        census["census_data"]["homo_sapiens"],
        measurement_name="RNA",
        X_name="raw",
        obs_value_filter="tissue_general == 'liver' and is_primary_data == True",
        obs_column_names=["cell_type"],
        batch_size=128,
        shuffle=True,
    )

    # Training loop
    for epoch in range(num_epochs):
        for batch in dataloader:
            X = batch["X"]  # Gene expression tensor
            labels = batch["obs"]["cell_type"]  # Cell type labels

            # Forward pass
            outputs = model(X)
            loss = criterion(outputs, labels)

            # Backward pass
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

Train/test splitting:

python

from cellxgene_census.experimental.ml import ExperimentDataset

对于模型训练，使用实验性的PyTorch集成：

python

from cellxgene_census.experimental.ml import experiment_dataloader

with cellxgene_census.open_soma() as census:
    # 创建数据加载器
    dataloader = experiment_dataloader(
        census["census_data"]["homo_sapiens"],
        measurement_name="RNA",
        X_name="raw",
        obs_value_filter="tissue_general == 'liver' and is_primary_data == True",
        obs_column_names=["cell_type"],
        batch_size=128,
        shuffle=True,
    )

    # 训练循环
    for epoch in range(num_epochs):
        for batch in dataloader:
            X = batch["X"]  # 基因表达张量
            labels = batch["obs"]["cell_type"]  # 细胞类型标签

            # 前向传播
            outputs = model(X)
            loss = criterion(outputs, labels)

            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

训练/测试拆分：

python

from cellxgene_census.experimental.ml import ExperimentDataset

Create dataset from experiment

从实验创建数据集

dataset = ExperimentDataset( experiment_axis_query, layer_name="raw", obs_column_names=["cell_type"], batch_size=128, )

Split into train and test

拆分为训练集和测试集

train_dataset, test_dataset = dataset.random_split( split=[0.8, 0.2], seed=42 )

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train_dataset, test_dataset = dataset.random_split( split=[0.8, 0.2], seed=42 )

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6. Integration with Scanpy

6. 与Scanpy集成

Seamlessly integrate Census data with scanpy workflows:

python

import scanpy as sc

将Census数据与scanpy工作流无缝集成：

python

import scanpy as sc

Load data from Census

从Census加载数据

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="cell_type == 'neuron' and tissue_general == 'cortex' and is_primary_data == True", )

Standard scanpy workflow

标准scanpy工作流

sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) sc.pp.highly_variable_genes(adata, n_top_genes=2000)

Dimensionality reduction

降维

sc.pp.pca(adata, n_comps=50) sc.pp.neighbors(adata) sc.tl.umap(adata)

Visualization

可视化

sc.pl.umap(adata, color=["cell_type", "tissue", "disease"])

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sc.pl.umap(adata, color=["cell_type", "tissue", "disease"])

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7. Multi-Dataset Integration

7. 多数据集集成

Query and integrate multiple datasets:

python

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查询并集成多个数据集：

python

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Strategy 1: Query multiple tissues separately

策略1：分别查询多个组织

tissues = ["lung", "liver", "kidney"] adatas = []

for tissue in tissues: adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter=f"tissue_general == '{tissue}' and is_primary_data == True", ) adata.obs["tissue"] = tissue adatas.append(adata)

tissues = ["lung", "liver", "kidney"] adatas = []

Concatenate

合并数据

combined = adatas[0].concatenate(adatas[1:])

Strategy 2: Query multiple datasets directly

策略2：直接查询多个数据集

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="tissue_general in ['lung', 'liver', 'kidney'] and is_primary_data == True", )

undefined

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="tissue_general in ['lung', 'liver', 'kidney'] and is_primary_data == True", )

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Key Concepts and Best Practices

核心概念与最佳实践

Always Filter for Primary Data

始终筛选原始数据

Unless analyzing duplicates, always include

is_primary_data == True

in queries to avoid counting cells multiple times:

python

obs_value_filter="cell_type == 'B cell' and is_primary_data == True"

除非分析重复细胞，否则始终在查询中包含

is_primary_data == True

以避免重复统计细胞：

python

obs_value_filter="cell_type == 'B cell' and is_primary_data == True"

Specify Census Version for Reproducibility

指定Census版本以保证可复现性

Always specify the Census version in production analyses:

python

census = cellxgene_census.open_soma(census_version="2023-07-25")

在生产分析中始终指定Census版本：

python

census = cellxgene_census.open_soma(census_version="2023-07-25")

Estimate Query Size Before Loading

加载前预估查询规模

For large queries, first check the number of cells to avoid memory issues:

python

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对于大型查询，先检查细胞数量以避免内存问题：

python

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Get cell count

获取细胞数量

metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="tissue_general == 'brain' and is_primary_data == True", column_names=["soma_joinid"] ) n_cells = len(metadata) print(f"Query will return {n_cells:,} cells")

If too large (>100k), use out-of-core processing

如果数量过大（>10万），使用核外处理

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Use tissue_general for Broader Groupings

使用tissue_general进行更广泛的分组

The

tissue_general

field provides coarser categories than

tissue

, useful for cross-tissue analyses:

python

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tissue_general

字段提供比

tissue

更粗略的分类，适用于跨组织分析：

python

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Broader grouping

更广泛的分组

obs_value_filter="tissue_general == 'immune system'"

Specific tissue

特定组织

obs_value_filter="tissue == 'peripheral blood mononuclear cell'"

undefined

obs_value_filter="tissue == 'peripheral blood mononuclear cell'"

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Select Only Needed Columns

仅选择所需列

Minimize data transfer by specifying only required metadata columns:

python

obs_column_names=["cell_type", "tissue_general", "disease"]  # Not all columns

通过仅指定所需的元数据列来减少数据传输：

python

obs_column_names=["cell_type", "tissue_general", "disease"]  # 不选择所有列

Check Dataset Presence for Gene-Specific Queries

针对特定基因查询时检查数据集覆盖情况

When analyzing specific genes, verify which datasets measured them:

python

presence = cellxgene_census.get_presence_matrix(
    census,
    "homo_sapiens",
    var_value_filter="feature_name in ['CD4', 'CD8A']"
)

分析特定基因时，验证哪些数据集测量了这些基因：

python

presence = cellxgene_census.get_presence_matrix(
    census,
    "homo_sapiens",
    var_value_filter="feature_name in ['CD4', 'CD8A']"
)

Two-Step Workflow: Explore Then Query

两步工作流：先探索再查询

First explore metadata to understand available data, then query expression:

python

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先探索元数据以了解可用数据，再查询表达数据：

python

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Step 1: Explore what's available

步骤1：探索可用数据

metadata = cellxgene_census.get_obs( census, "homo_sapiens", value_filter="disease == 'COVID-19' and is_primary_data == True", column_names=["cell_type", "tissue_general"] ) print(metadata.value_counts())

Step 2: Query based on findings

步骤2：基于发现结果进行查询

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="disease == 'COVID-19' and cell_type == 'T cell' and is_primary_data == True", )

undefined

adata = cellxgene_census.get_anndata( census=census, organism="Homo sapiens", obs_value_filter="disease == 'COVID-19' and cell_type == 'T cell' and is_primary_data == True", )

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Available Metadata Fields

可用元数据字段

Cell Metadata (obs)

细胞元数据（obs）

Key fields for filtering:

```
cell_type
```
,
```
cell_type_ontology_term_id
```

tissue

tissue_general

tissue_ontology_term_id

```
disease
```
,
```
disease_ontology_term_id
```
```
assay
```
,
```
assay_ontology_term_id
```
```
donor_id
```
,
```
sex
```
,
```
self_reported_ethnicity
```

development_stage

development_stage_ontology_term_id

```
dataset_id
```
```
is_primary_data
```
(Boolean: True = unique cell)

用于筛选的关键字段：

```
cell_type
```
,
```
cell_type_ontology_term_id
```

tissue

tissue_general

tissue_ontology_term_id

```
disease
```
,
```
disease_ontology_term_id
```
```
assay
```
,
```
assay_ontology_term_id
```
```
donor_id
```
,
```
sex
```
,
```
self_reported_ethnicity
```

development_stage

development_stage_ontology_term_id

```
dataset_id
```
```
is_primary_data
```
（布尔值：True = 唯一细胞）

Gene Metadata (var)

基因元数据（var）

```
feature_id
```
(Ensembl gene ID, e.g., "ENSG00000161798")
```
feature_name
```
(Gene symbol, e.g., "FOXP2")
```
feature_length
```
(Gene length in base pairs)

```
feature_id
```
（Ensembl基因ID，例如"ENSG00000161798"）
```
feature_name
```
（基因符号，例如"FOXP2"）
```
feature_length
```
（基因长度，单位为碱基对）

Reference Documentation

参考文档

This skill includes detailed reference documentation:

本技能包含详细的参考文档：

references/census_schema.md

Comprehensive documentation of:

Census data structure and organization
All available metadata fields
Value filter syntax and operators
SOMA object types
Data inclusion criteria

When to read: When you need detailed schema information, full list of metadata fields, or complex filter syntax.

全面文档涵盖：

Census数据结构与组织
所有可用元数据字段
值筛选语法与运算符
SOMA对象类型
数据纳入标准

阅读时机： 当你需要详细的 schema 信息、完整的元数据字段列表或复杂筛选语法时。

references/common_patterns.md

Examples and patterns for:

Exploratory queries (metadata only)
Small-to-medium queries (AnnData)
Large queries (out-of-core processing)
PyTorch integration
Scanpy integration workflows
Multi-dataset integration
Best practices and common pitfalls

When to read: When implementing specific query patterns, looking for code examples, or troubleshooting common issues.

包含以下场景的示例与模式：

探索性查询（仅元数据）
中小规模查询（AnnData）
大规模查询（核外处理）
PyTorch集成
Scanpy集成工作流
多数据集集成
最佳实践与常见陷阱

阅读时机： 当你实现特定查询模式、寻找代码示例或排查常见问题时。

Common Use Cases

常见用例

Use Case 1: Explore Cell Types in a Tissue

用例1：探索某一组织中的细胞类型

python

with cellxgene_census.open_soma() as census:
    cells = cellxgene_census.get_obs(
        census, "homo_sapiens",
        value_filter="tissue_general == 'lung' and is_primary_data == True",
        column_names=["cell_type"]
    )
    print(cells["cell_type"].value_counts())

python

with cellxgene_census.open_soma() as census:
    cells = cellxgene_census.get_obs(
        census, "homo_sapiens",
        value_filter="tissue_general == 'lung' and is_primary_data == True",
        column_names=["cell_type"]
    )
    print(cells["cell_type"].value_counts())

Use Case 2: Query Marker Gene Expression

用例2：查询标记基因表达

python

with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19']",
        obs_value_filter="cell_type in ['T cell', 'B cell'] and is_primary_data == True",
    )

python

with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        var_value_filter="feature_name in ['CD4', 'CD8A', 'CD19']",
        obs_value_filter="cell_type in ['T cell', 'B cell'] and is_primary_data == True",
    )

Use Case 3: Train Cell Type Classifier

用例3：训练细胞类型分类器

python

from cellxgene_census.experimental.ml import experiment_dataloader

with cellxgene_census.open_soma() as census:
    dataloader = experiment_dataloader(
        census["census_data"]["homo_sapiens"],
        measurement_name="RNA",
        X_name="raw",
        obs_value_filter="is_primary_data == True",
        obs_column_names=["cell_type"],
        batch_size=128,
        shuffle=True,
    )

    # Train model
    for epoch in range(epochs):
        for batch in dataloader:
            # Training logic
            pass

python

from cellxgene_census.experimental.ml import experiment_dataloader

with cellxgene_census.open_soma() as census:
    dataloader = experiment_dataloader(
        census["census_data"]["homo_sapiens"],
        measurement_name="RNA",
        X_name="raw",
        obs_value_filter="is_primary_data == True",
        obs_column_names=["cell_type"],
        batch_size=128,
        shuffle=True,
    )

    # 训练模型
    for epoch in range(epochs):
        for batch in dataloader:
            # 训练逻辑
            pass

Use Case 4: Cross-Tissue Analysis

用例4：跨组织分析

python

with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'macrophage' and tissue_general in ['lung', 'liver', 'brain'] and is_primary_data == True",
    )

    # Analyze macrophage differences across tissues
    sc.tl.rank_genes_groups(adata, groupby="tissue_general")

python

with cellxgene_census.open_soma() as census:
    adata = cellxgene_census.get_anndata(
        census=census,
        organism="Homo sapiens",
        obs_value_filter="cell_type == 'macrophage' and tissue_general in ['lung', 'liver', 'brain'] and is_primary_data == True",
    )

    # 分析跨组织巨噬细胞的差异
    sc.tl.rank_genes_groups(adata, groupby="tissue_general")

Troubleshooting

故障排除

Query Returns Too Many Cells

查询返回过多细胞

Add more specific filters to reduce scope
Use
```
tissue
```
instead of
```
tissue_general
```
for finer granularity
Filter by specific
```
dataset_id
```
if known
Switch to out-of-core processing for large queries

添加更具体的筛选条件以缩小范围
使用
```
tissue
```
而非
```
tissue_general
```
以获得更精细的粒度
如果已知，按特定
```
dataset_id
```
筛选
对于大型查询切换到核外处理

Memory Errors

内存错误

Reduce query scope with more restrictive filters
Select fewer genes with
```
var_value_filter
```
Use out-of-core processing with
```
axis_query()
```
Process data in batches

使用更严格的筛选条件缩小查询范围
使用
```
var_value_filter
```
选择更少的基因
使用
```
axis_query()
```
进行核外处理
分块处理数据

Duplicate Cells in Results

结果中出现重复细胞

Always include
```
is_primary_data == True
```
in filters
Check if intentionally querying across multiple datasets

始终在筛选条件中包含
```
is_primary_data == True
```
检查是否有意跨多个数据集查询

Gene Not Found

未找到基因

Verify gene name spelling (case-sensitive)
Try Ensembl ID with
```
feature_id
```
instead of
```
feature_name
```
Check dataset presence matrix to see if gene was measured
Some genes may have been filtered during Census construction

验证基因名称拼写（区分大小写）
尝试使用Ensembl ID（
```
feature_id
```
）而非
```
feature_name
```
检查数据集存在矩阵以确认基因是否被测量
部分基因可能在Census构建过程中被过滤

Version Inconsistencies

版本不一致

Always specify
```
census_version
```
explicitly
Use same version across all analyses
Check release notes for version-specific changes

始终显式指定
```
census_version
```
在所有分析中使用相同版本
查看发行说明了解版本特定变更