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aaronwolen / download_visium.py
Last active November 7, 2025 18:50
Download 10X Visium output files for squidpy example datasets.
#!/usr/bin/env python3
"""
Download 10X Visium datasets using squidpy.
This tool wraps squidpy's internal visium() function to provide an easy way
to download and extract 10X Genomics Visium spatial transcriptomics datasets.
"""
from __future__ import annotations
import argparse
@aaronwolen
aaronwolen / cross-tissue_submanifold-analysis.ipynb
Created November 5, 2025 23:14
Cross-Tissue Macrophage Submanifold Analysis
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@aaronwolen
aaronwolen / output.txt
Last active April 11, 2024 14:23
tiledbsoma query stats
tiledbsoma.__version__ 1.5.0rc0.post289.dev1706826235
TileDB-Py tiledb.version() (0, 27, 1)
TileDB core version 2.21.1
libtiledbsoma version() libtiledb=2.21.1
python version 3.11.8.final.0
OS version Darwin 23.3.0
Checking tiledb...
@aaronwolen
aaronwolen / tiledb-batched-reads.R
Last active November 14, 2023 21:35
Example using TileDB-R's batched reader API.
#! /usr/bin/env Rscript
library(tiledb)
library(data.table)
library(nycflights13)
TILEDB_URI <- file.path(tempdir(), "tiledb-flights")
if (dir.exists(TILEDB_URI)) unlink(TILEDB_URI, recursive = TRUE)
STATS_DIR <- file.path(getwd(), "stats")
@aaronwolen
aaronwolen / README.md
Last active May 30, 2023 15:52
Quokka filters

Quokka Filters

@aaronwolen
aaronwolen / tiledb-dense-array-queries.md
Created April 7, 2023 14:56
Quick examples of querying dense arrays with TileDB-R

TileDB Dense Array Queries

Setup

Create a dense matrix random integers.

library(tiledb)
set.seed(123)
#!/usr/bin/env Rscript
library(docopt)
# Changelog
# ---------
# 0.1.1: Consolidate fragment metadata and commits after ingestion
# 0.1.0: Initial release
# docopt ------------------------------------------------------------------
@aaronwolen
aaronwolen / README.md
Last active July 11, 2022 12:52
allen-institute-merscope

README

Setup

Use conda to install the TileDB Python API and other dependencies.

mamba env create -f environment.yaml
conda activate aind-demo

Our starting point is raw counts in a sparse matrix, smat (4 genes, 5 cells):

   c1 c2 c3 c4 c5
g1  8  .  .  .  5
g2  .  5  7  .  .
g3  1  .  .  5  .
g4  8  .  .  7  .
13,15c13,17
< dtype=object)), ('real_start_pos', array([43338668, 43341403, 43341867, 43342848, 43342933, 43345733,
< 43346403, 43349192, 43350860, 43351009], dtype=uint32)), ('end_pos', array([43338668, 43341403, 43341867, 43342848, 43342934, 43345733,
< 43346403, 43349192, 43350860, 43351019], dtype=uint32)), ('qual', array([ 49.71, 50. , 50. , 50. , 50. , 49.27, 49.57, 46.14,
---
> dtype=object)), ('real_start_pos', array([ 393434177, 2913177362, 3658721541, 2359512986, 4034073792,
> 2945374281, 1320880414, 2723889732, 1316948380, 1664381440],
> dtype=uint32)), ('end_pos', array([ 393434177, 2913177168, 3658721541, 2359496344, 4034073792,
> 2408503627, 1320880152, 2723889732, 1325336600, 1664381440],
> dtype=uint32)), ('qual', array([ 49.71, 50. , 50. , 50. , 50. , 49.27, 49.57, 46.14,