Extensive immune infiltration refers to the widespread presence of immune cells within a tissue, often observed in inflammatory conditions, infections, or tumors. Typically, this infiltration is dominated by lymphocytes (T cells, B cells), macrophages, and dendritic cells, which localize near sites of antigen presentation, damaged cells, or neoplastic growth. In cancers, immune cells may cluster around the tumor periphery (immune-excluded) or penetrate deeply (inflamed phenotype). Chronic inflammation often shows diffuse infiltration across tissues. The distribution reflects the immune system's attempt to combat threats or maintain homeostasis, influenced by cytokine gradients, chemokine signals, and tissue-specific factors shaping immune cell recruitment and retention.

🌵Python snippet

🍂using scRNA-seq data

Single-cell RNA sequencing (scRNA-seq) data analysis involves many steps, from data preprocessing to visualization and downstream analysis. Here’s a Python example demonstrating some basic tasks like loading, preprocessing, and visualizing scRNA-seq data using the Scanpy library.

Example: Basic scRNA-seq Analysis Pipeline with Scanpy

 import scanpy as sc
 import matplotlib.pyplot as plt
 ​
 # Step 1: Load a dataset
 # For simplicity, use a built-in dataset from Scanpy
 adata = sc.datasets.pbmc3k_processed()  # Preprocessed PBMC dataset
 ​
 # Inspect the data
 print(adata)  # Observations (cells) × Variables (genes)
 ​
 # Step 2: Preprocess data
 # Filter genes and cells based on minimum expression
 sc.pp.filter_genes(adata, min_cells=10)  # Keep genes expressed in at least 10 cells
 sc.pp.filter_cells(adata, min_genes=200)  # Keep cells with at least 200 expressed genes
 ​
 # Normalize the data
 sc.pp.normalize_total(adata, target_sum=1e4)  # Normalize total counts per cell
 sc.pp.log1p(adata)  # Logarithmic transformation
 ​
 # Identify highly variable genes
 sc.pp.highly_variable_genes(adata, n_top_genes=2000)
 adata = adata[:, adata.var['highly_variable']]  # Subset to these genes
 ​
 # Scale the data
 sc.pp.scale(adata)
 ​
 # Step 3: Dimensionality Reduction
 # Perform PCA
 sc.tl.pca(adata, svd_solver='arpack')
 sc.pl.pca(adata, color='CST3')  # Plot PCA with a gene marker
 ​
 # Compute neighborhood graph
 sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)
 ​
 # Perform UMAP for visualization
 sc.tl.umap(adata)
 sc.pl.umap(adata, color=['louvain', 'CST3'])  # Visualize clusters and gene expression
 ​
 # Step 4: Clustering
 # Use the Louvain algorithm for clustering
 sc.tl.louvain(adata)
 sc.pl.umap(adata, color='louvain')  # Visualize clusters
 ​
 # Step 5: Differential Expression Analysis
 # Find marker genes for clusters
 sc.tl.rank_genes_groups(adata, groupby='louvain', method='wilcoxon')
 sc.pl.rank_genes_groups(adata, n_genes=10, sharey=False)

Explanation of Steps:

1. Loading Data:
   • Uses a preprocessed scRNA-seq dataset (PBMC 3k cells) as an example. Alternatively, use custom datasets in .h5ad or .mtx formats.
2. Preprocessing:
   • Filters low-quality genes and cells.
   • Normalizes expression counts and applies logarithmic transformation.
   • Identifies highly variable genes for downstream analysis.
3. Dimensionality Reduction:
   • Reduces data dimensionality using PCA.
   • Visualizes data with UMAP, preserving local structures.
4. Clustering:
   • Groups cells into clusters based on their expression profiles.
5. Differential Expression Analysis:
   • Identifies marker genes distinguishing clusters using statistical tests.

This pipeline demonstrates the fundamentals of scRNA-seq data analysis. Customize it with your data for more advanced workflows like trajectory analysis, integration, or cell type annotation.

🍂Using Bulk RNA-seq data

Bulk RNA-seq data analysis involves quantifying gene expression across a population of cells, identifying differentially expressed genes, and performing downstream analyses like functional enrichment. Below is an example Python pipeline for bulk RNA-seq analysis using some common Python libraries:

Example Workflow

This example demonstrates how to preprocess RNA-seq count data, perform differential expression analysis, and visualize the results.

1. Install Required Libraries

Make sure you have the following Python packages installed: