How Does Non-negative Matrix Factorization (NMF) Apply to scRNA-seq Cell Clustering?
Non-negative Matrix Factorization (NMF) is a matrix factorization technique used to decompose a data matrix into the product of two or more smaller matrices with non-negative elements. NMF is particularly suitable for data mining and feature extraction because it preserves the structure and interpretability of the data.
scRNA-seq data is typically represented by a high-dimensional matrix, where each row corresponds to a gene and each column represents the gene expression profile of a single cell. When applied to scRNA-seq data, NMF aims to decompose the original gene expression matrix into two matrices: the gene factor matrix and the cell coefficient matrix. The gene factor matrix represents gene sets that may correspond to biological processes or cellular states, while the cell coefficient matrix describes the activity level of each cell across these gene sets.
By performing clustering analysis on the cell coefficient matrix, researchers can identify cells with similar expression patterns, thus recognizing cell subpopulations. This helps in understanding cellular heterogeneity in tissues and discovering new cell types.
Since NMF produces only non-negative components, it is particularly useful for gene expression data, which is naturally non-negative. Additionally, another advantage of NMF is that its results are easy to interpret, as gene sets can be viewed as the fundamental building blocks of cellular expression profiles.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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