Deconvolution algorithms use mathematical models to analyze gene expression data or other molecular data from bulk tumor samples. These models separate the data into signals corresponding to different cell types. Common techniques include:
Linear Regression: This method estimates the proportions of different cell types by fitting a linear model to the gene expression data. Non-negative Matrix Factorization (NMF): NMF decomposes the data matrix into two non-negative matrices, representing cell type-specific gene expression profiles and their proportions. Bayesian Methods: These methods incorporate prior knowledge and uncertainty into the deconvolution process, providing more robust estimates. Machine Learning: Advanced machine learning techniques, including deep learning, are used for more accurate and complex deconvolution tasks.
