genotype-hypothesis-analysis

name	genotype-hypothesis-analysis
description	Multi-hypothesis genotype analysis framework for spatial biology data with small sample sizes. Trigger: (1) comparing measurements across genotype groups with small n (3-4/group), (2) multi-notebook analysis of spatial/multiplex imaging annotations, (3) vessel/region density normalized across tissue compartments.
author	smith6jt
date	"2026-02-26T00:00:00.000Z"

Genotype Hypothesis Analysis - Research Notes

Experiment Overview

Item	Details
Date	2026-02-26
Goal	Build a reusable multi-hypothesis analysis framework for comparing spatial biology measurements across genotype groups (rs3184504 C/C, C/T, T/T)
Environment	Python 3.12, pandas 2.x, scipy, seaborn, matplotlib, Jupyter notebooks
Status	Success

Context

QuPath-segmented multiplex imaging data (Phenocycler spleen tissue) with ~500K annotations across 13 images from 10 samples. Need to compare vessel density, morphology, tissue architecture, and follicle vascularization across 3 genotype groups with very small n (3-4 per group). Standard parametric tests are inappropriate; need non-parametric framework with effect size reporting.

Verified Workflow

1. Shared data_utils.py module pattern

Centralize all data loading, genotype mapping, filtering, and statistics in one module imported by all notebooks:

# analysis/data_utils.py — key functions:
# load_data() → reads CSV, adds Sample/Genotype columns, drops exclusions
# get_regions(df) / get_vessels(df) → filter by Classification
# compute_density(df) → vessel count / region area (mm²), with RedPulp normalization
# assign_vessels_to_follicles(df, image) → cKDTree nearest-centroid assignment
# full_stats_table(data, value_col) → Kruskal-Wallis + pairwise Mann-Whitney + Spearman dosage

2. Sample ID extraction from heterogeneous image names

def extract_sample_id(image_name):
    """Handles HDL011_PC33.ome.tiff, HDL052SPLN_2025Aug6_Scan1.er.qptiff, 1901HBMP004_PC29.ome.tiff"""
    m = re.match(r"(HDL\d+)", image_name)
    if m: return m.group(1)
    m = re.match(r"(\d{4}HBMP\d+)", image_name)
    if m: return m.group(1)
    return image_name.split("_")[0]

3. Statistical framework for small n

# Omnibus: Kruskal-Wallis (non-parametric ANOVA)
# Pairwise: Mann-Whitney U with rank-biserial effect size r = 1 - 2U/(n1*n2)
# Gene dosage: Spearman correlation with ordinal encoding (C/C=0, C/T=1, T/T=2)
# Unit of analysis: per-image aggregates, NOT individual annotations

4. Density normalization

# RedPulp normalization: density_normalized = density / redpulp_density_same_image
# This controls for image-level variation in overall vessel detection sensitivity

5. Spatial vessel-to-follicle assignment (cKDTree)

from scipy.spatial import cKDTree
fol_coords = follicles[["Centroid X µm", "Centroid Y µm"]].values
tree = cKDTree(fol_coords)
_, indices = tree.query(vessel_coords)
# Assign each vessel to nearest follicle centroid

Failed Attempts (Critical)

Attempt	Why it Failed	Lesson Learned
pandas Categorical genotype with default groupby()	Creates cross-product of all categories × groups, producing NaN rows and memory bloat	Always use observed=True with Categorical columns in groupby
Using individual annotations as units of analysis	Pseudoreplication — thousands of vessels per image are not independent	Aggregate to per-image statistics (median, count) first, then compare groups
AllAnnotations.csv as data source	Missing newer images (HDL053, HDL070, HDL073, 1901HBMP004)	Use AnnotationsFinal.csv which includes all 13 images
Violin plots as primary statistical evidence	Misleading with small n — shows vessel-level distributions, not sample-level	Use boxplot+strip of per-image aggregates; violins are "visual only" with caveat
Parametric tests (t-test, ANOVA)	n=3-4 per group violates normality assumptions	Kruskal-Wallis + Mann-Whitney; report effect sizes over p-values

Final Parameters

# Statistical framework
omnibus_test: kruskal-wallis
pairwise_test: mann-whitney-u (two-sided)
effect_size: rank-biserial (r = 1 - 2U/n1n2)
dosage_trend: spearman correlation
unit_of_analysis: per-image aggregate

# Density computation
area_unit: mm² (divide µm² by 1e6)
normalization: RedPulp density per image
main_regions: [Follicle, PALS, RedPulp, Trabeculae]

# Vessel morphology
metrics: [Area, Circularity, Solidity, Elongation, Max_diameter, Min_diameter]
elongation: Max_diameter / Min_diameter
size_bins_um2: [0, 50, 200, inf]  # Small, Medium, Large

# Spatial assignment
method: scipy.spatial.cKDTree nearest-centroid
follicle_size_stratification: tertiles (qcut q=3)

# Plotting
style: seaborn whitegrid, font_scale=1.1
palette: Set2 (3 colors for 3 genotypes)
figure_dpi: 150
format: PNG

Key Insights

• Effect sizes > p-values with n=3-4 per group — include this caveat prominently
• RedPulp normalization controls for batch/image-level detection sensitivity differences
• cKDTree spatial assignment works well when follicles are well-separated (r=0.76 area-count correlation validates approach)
• Shared module pattern (data_utils.py) eliminates code duplication and ensures consistency across 5+ notebooks
• Per-image medians are more robust than means for morphology metrics with heavy-tailed distributions
• Include zero-vessel follicles in per-follicle analyses — left-join with all follicles to avoid survivorship bias
• Gene dosage trend (Spearman with ordinal encoding) is particularly informative for allele-dosage hypotheses

References

• SH2B3/LNK: negative regulator of JAK-STAT signaling
• rs3184504 T allele: R262W loss-of-function variant
• QuPath v0.6.0 for image segmentation
• SpleenFollicleCounterQP project