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Editorial changes to notebook census_axis_query.ipynb #371

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Apr 11, 2023
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Editorial changes to notebook census_axis_query.ipynb #371

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f40bc1e
editorial changes to notebook
pablo-gar Apr 11, 2023
a90fc36
Update api/python/notebooks/api_demo/census_axis_query.ipynb
pablo-gar Apr 11, 2023
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33 changes: 20 additions & 13 deletions api/python/notebooks/api_demo/census_axis_query.ipynb
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Original file line number Diff line number Diff line change
@@ -1,17 +1,23 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Axis Query Example\n",
"# Querying axis metadata\n",
"\n",
"_Goal:_ demonstrate basic axis metadata handling using Pandas.\n",
"This notebook provides examples for basic axis metadata handling using Pandas. The Census stores `obs` (cell) and `var` (gene) metadata in `SOMADataFrame` objects via the [`TileDB-SOMA` API](https://github.com/single-cell-data/TileDB-SOMA) ([documentation](https://tiledbsoma.readthedocs.io/en/latest/)), which can be queried and read as a Pandas `DataFrame` using `TileDB-SOMA`. \n",
"\n",
"The Census stores obs (cell) metadata in a SOMA DataFrame, which can be queried and read as a Pandas DataFrame. The Census also has a convenience package which simplifies opening the census.\n",
"Note that Pandas `DataFrame` is an in-memory object, therefore queries should be small enough for results to fit in memory.\n",
"\n",
"**Contents**\n",
"\n",
"1. Opening the Census\n",
"1. Summarizing cell metadata\n",
" 1. Example: Summarize all cell types\n",
" 1. Example: Summarize a subset of cell types, selected with a `value_filter`\n",
"1. Full Census metadata stats\n",
"\n",
"Pandas DataFrame is an in-memory object. Take care that queries are small enough for results to fit in memory.\n",
"\n",
"## Opening the Census\n",
"\n",
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Summarize Census cell metadata\n",
"## Summarizing cell metadata\n",
"\n",
"Once the Census is open you can use its `TileDB-SOMA` methods as it is itself a `SOMACollection`. You can thus access the metadata `SOMADataFrame` objects encoding cell and gene metadata.\n",
"\n",
"Tips:\n",
"\n",
"- You can read an _entire_ SOMA dataframe into a Pandas DataFrame using `soma_df.read().concat().to_pandas()`, allowing the use of the standard Pandas API.\n",
"- You can read an _entire_ `SOMADataFrame` into a Pandas `DataFrame` using `soma_df.read().concat().to_pandas()`, allowing the use of the standard Pandas API.\n",
"- Queries will be much faster if you request only the DataFrame columns required for your analysis (e.g., `column_names=[\"cell_type_ontology_term_id\"]`).\n",
"- You can also further refine query results by using a `value_filter`, which will filter the census for matching records.\n",
"\n",
"### Example 1 - Summarize all cell types\n",
"### Example: Summarize all cell types\n",
"\n",
"This example reads the cell metadata (obs) into a Pandas DataFrame, and summarizes in a variety of ways using Pandas API."
"This example reads the cell metadata (`obs`) into a Pandas DataFrame, and summarizes in a variety of ways using Pandas API."
]
},
{
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"cell_type": "markdown",
"metadata": {},
"source": [
"### Summarize a subset of cell types, selected with a `value_filter`\n",
"### Example: Summarize a subset of cell types, selected with a `value_filter`\n",
"\n",
"This example utilizes a SOMA \"value filter\" to read the subset of cells with `tissue_ontology_term_id` equal to `UBERON:0002048` (lung tissue), and summarizes the query result using Pandas."
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Full census stats\n",
"## Full Census metadata stats\n",
"\n",
"This example queries all organisms in the Census, and summarizes the diversity of various metadata lables."
]
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.6"
"version": "3.10.10"
},
"vscode": {
"interpreter": {
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