bulkrna-deconvolution

You can estimate cell type proportions from bulk RNA-seq data by applying Non-Negative Least Squares (NNLS) deconvolution against a cell type signature matrix. This approach analyzes gene expression matrices to determine the relative abundance of specific cell populations.

A signature matrix in bulk RNA-seq deconvolution serves as the reference profile of pure cell types. The NNLS algorithm compares your bulk gene expression data against this signature matrix to mathematically separate and estimate the cellular composition of mixed tissue samples.

Yes, you can use CIBERSORTx or MuSiC for advanced cell type deconvolution. This approach provides optional integration bridges to these sophisticated methods, allowing you to enhance estimation accuracy beyond the standard Non-Negative Least Squares baseline.

No, you do not need single-cell data to perform RNA-seq deconvolution. You need a bulk RNA-seq gene expression matrix and a pre-defined cell type signature matrix to estimate tissue composition and cellular heterogeneity without requiring single-cell resolution.

The required Python dependencies for NNLS deconvolution of RNA-seq data include numpy, pandas, scipy, and matplotlib. These libraries provide the necessary data structures, mathematical optimization algorithms, and visualization capabilities for analyzing gene expression matrices.