densr 0.1.7

Density-Aware Spatial Randomization (DenSR) generates realistic null models for object-based colocalization analysis.

DenSR

DenSR provides density-aware spatial randomization using KDE-weighted sampling within a 3D cell mesh, plus object-based colocalization analysis for observed RNA spot coordinates, UniSR, and DenSR.

Cite this package

Kannan, S., Voelker, T. L., Snyder, B. S., & Robertson, G. A. (2026). Density-Aware Spatial Randomization (DenSR) generates realistic null models for object-based colocalization analysis. Molecular Biology of the Cell, 37(6), mr6.

Install

pip install densr

Usage

from densr import densr

spots1 = "path/to/folder/C1_1a_xyz.txt"
spots2 = "path/to/folder/C1_1b_xyz.txt"
object = "path/to/folder/C1_volume.obj"
colocalization_threshold = 1

densr(
    spots1=spots1,
    spots2=spots2,
    object=object,
    colocalization_threshold=colocalization_threshold,
)

The RNA spot files are read as tab-separated text files, and the first three columns are used as x, y, and z coordinates. The densr(...) call displays the results table and spot plot automatically.

Built-in sample data

from densr import densr, sample_data

sample = sample_data()

densr(
    spots1=sample.spots1,
    spots2=sample.spots2,
    object=sample.object,
    colocalization_threshold=0.25,
)

Command line

densr C1_volume.obj C1_1a_xyz.txt C1_1b_xyz.txt --colocalization-threshold 1

Functions

• densr(spots1, spots2, object, colocalization_threshold=1): one-line DenSR analysis using RNA spot files and a 3D cell mesh file.
• sample_data(): return file paths for the built-in DenSR sample data.