Cell Phenotyping for IMC

Load Single-Cell Data

import anndata as ad import scanpy as sc import pandas as pd import numpy as np

Load from h5ad

adata = ad.read_h5ad('imc_segmented.h5ad')

Or create from CSVs

intensities = pd.read_csv('cell_intensities.csv') cell_info = pd.read_csv('cell_info.csv')

adata = ad.AnnData(X=intensities.values) adata.var_names = intensities.columns adata.obs = cell_info

Data Transformation

Arcsinh transformation (standard for cytometry)

def arcsinh_transform(adata, cofactor=5): adata.X = np.arcsinh(adata.X / cofactor) return adata

adata = arcsinh_transform(adata)

Z-score normalization

sc.pp.scale(adata, max_value=10)

Clustering-Based Phenotyping

PCA and neighbors

sc.pp.pca(adata, n_comps=15) sc.pp.neighbors(adata, n_neighbors=15, n_pcs=15)

Clustering

sc.tl.leiden(adata, resolution=0.5)

UMAP for visualization

sc.tl.umap(adata)

Plot

sc.pl.umap(adata, color='leiden', save='_clusters.png')

Manual Gating

def gate_cells(adata, marker, threshold, above=True): '''Gate cells based on marker expression''' values = adata[:, marker].X.flatten() if above: return values > threshold else: return values < threshold

Example gating strategy for T cells

adata.obs['CD45_pos'] = gate_cells(adata, 'CD45', 1.5) adata.obs['CD3_pos'] = gate_cells(adata, 'CD3', 1.0) adata.obs['CD8_pos'] = gate_cells(adata, 'CD8', 0.8) adata.obs['CD4_pos'] = gate_cells(adata, 'CD4', 0.8)

Assign cell types

def assign_cell_type(row): if not row['CD45_pos']: return 'Other' if not row['CD3_pos']: return 'Non-T immune' if row['CD8_pos']: return 'CD8 T cell' if row['CD4_pos']: return 'CD4 T cell' return 'T cell (other)'

adata.obs['cell_type'] = adata.obs.apply(assign_cell_type, axis=1)

Cluster Annotation

Find marker genes per cluster

sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon') sc.pl.rank_genes_groups_heatmap(adata, n_genes=5, save='_markers.png')

Manual annotation based on markers

cluster_annotation = { '0': 'Epithelial', '1': 'CD8 T cell', '2': 'CD4 T cell', '3': 'Macrophage', '4': 'Stromal', '5': 'B cell' }

adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_annotation)

SOM-Based Clustering (FlowSOM-Style)

FlowSOM-style clustering using minisom

Note: For authentic FlowSOM, use the R CATALYST package which wraps FlowSOM

This Python approach approximates the SOM + meta-clustering concept

from minisom import MiniSom from sklearn.cluster import AgglomerativeClustering

Markers for clustering

phenotype_markers = ['CD45', 'CD3', 'CD8', 'CD4', 'CD20', 'CD68', 'E-cadherin'] X = adata[:, phenotype_markers].X

Self-Organizing Map

som = MiniSom(10, 10, X.shape[1], sigma=1.5, learning_rate=0.5) som.random_weights_init(X) som.train_random(X, 1000)

Get cluster assignments

winner_coordinates = np.array([som.winner(x) for x in X]) som_clusters = winner_coordinates[:, 0] * 10 + winner_coordinates[:, 1]

Meta-clustering

meta_clustering = AgglomerativeClustering(n_clusters=10) meta_labels = meta_clustering.fit_predict(som.get_weights().reshape(-1, X.shape[1]))

Assign to cells

adata.obs['som_cluster'] = [meta_labels[c] for c in som_clusters]

Automated Annotation

Use reference-based annotation (similar to CellTypist)

from sklearn.neighbors import KNeighborsClassifier

If you have a reference dataset with known labels

ref_data = ad.read_h5ad('reference_imc.h5ad')

Train classifier

knn = KNeighborsClassifier(n_neighbors=15) knn.fit(ref_data.X, ref_data.obs['cell_type'])

Predict

adata.obs['predicted_type'] = knn.predict(adata.X) adata.obs['prediction_prob'] = knn.predict_proba(adata.X).max(axis=1)

Visualize Phenotypes

import matplotlib.pyplot as plt

UMAP colored by cell type

sc.pl.umap(adata, color='cell_type', save='_celltypes.png')

Heatmap of markers by cell type

sc.pl.matrixplot(adata, phenotype_markers, groupby='cell_type', dendrogram=True, cmap='RdBu_r', save='_heatmap.png')

Spatial plot colored by cell type

fig, ax = plt.subplots(figsize=(10, 10)) spatial = adata.obsm['spatial'] for ct in adata.obs['cell_type'].unique(): mask = adata.obs['cell_type'] == ct ax.scatter(spatial[mask, 0], spatial[mask, 1], s=1, label=ct, alpha=0.7) ax.legend(markerscale=5) ax.set_aspect('equal') plt.savefig('spatial_celltypes.png', dpi=150)

Cell Type Frequencies

Frequencies per image/ROI

freq = adata.obs.groupby(['image_id', 'cell_type']).size().unstack(fill_value=0) freq_pct = freq.div(freq.sum(axis=1), axis=0) * 100

Plot

freq_pct.plot(kind='bar', stacked=True, figsize=(12, 6)) plt.ylabel('Percentage') plt.title('Cell Type Composition') plt.tight_layout() plt.savefig('celltype_frequencies.png')

Save Results

Add annotations to adata

adata.write('imc_phenotyped.h5ad')

Export cell types

adata.obs[['cell_id', 'cell_type', 'centroid_x', 'centroid_y']].to_csv('cell_phenotypes.csv', index=False)

Related Skills

• cell-segmentation - Generate single-cell data

• spatial-analysis - Analyze spatial patterns of cell types

• single-cell/cell-annotation - Similar annotation concepts