====== RNA Sequencing-Based Immune Cell Deconvolution ======

**Definition**  

[[RNA sequencing]]-based immune cell [[deconvolution]] is a computational method that estimates the proportion and types of immune cells present in bulk tissue RNA-seq data. It allows researchers to infer immune composition from gene expression profiles, without the need for single-cell or flow cytometry data.

=== Key Concepts ===
  * Works on **bulk RNA-seq data**, which includes mixed cell populations  
  * Uses known **immune cell gene signatures** to deconvolute expression  
  * Output is typically a **cell type proportion matrix** (e.g. % CD8+ T cells, % macrophages)  

=== Common Tools ===
  * **CIBERSORT / CIBERSORTx** – Reference-based method using a leukocyte signature matrix (LM22)  
  * **xCell** – Uses gene set enrichment to score cell types  
  * **EPIC** – Designed for tumor environments  
  * **MCP-counter** – Estimates abundance of immune and stromal populations  
  * **TIMER** – Focused on tumor immune estimation across cancer types  

=== Applications ===
  * Characterize the **tumor immune microenvironment (TIME)**  
  * Predict response to **immunotherapy**  
  * Stratify patients based on immune infiltration patterns  
  * Complement histological or flow-based findings  

=== Example Insight ===
In lung adenocarcinoma RNA-seq data, CIBERSORT may reveal elevated M2 macrophages and reduced CD8+ T cells in non-responders to PD-1 blockade therapy.

=== Limitations ===
  * Accuracy depends on the quality of the reference signature  
  * Cannot capture spatial information  
  * Performance can vary with tumor heterogeneity or stromal contamination