About: Cellular deconvolution

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Cellular deconvolution (also referred to as cell type composition or cell proportion estimation) refers to computational techniques aiming at estimating the proportions of different cell types in samples collected from a tissue. For example, samples collected from the human brain are a mixture of various neuronal and glial cell types (e.g. microglia and astrocytes) in different proportions, where each cell type has a diverse gene expression profile. Since most high-throughput technologies use bulk samples and measure the aggregated levels of molecular information (e.g. expression levels of genes) for all cells in a sample, the measured values would be an aggregate of the values pertaining to the expression landscape of different cell types. Therefore, many downstream analyses such as migh

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  • Cellular deconvolution (also referred to as cell type composition or cell proportion estimation) refers to computational techniques aiming at estimating the proportions of different cell types in samples collected from a tissue. For example, samples collected from the human brain are a mixture of various neuronal and glial cell types (e.g. microglia and astrocytes) in different proportions, where each cell type has a diverse gene expression profile. Since most high-throughput technologies use bulk samples and measure the aggregated levels of molecular information (e.g. expression levels of genes) for all cells in a sample, the measured values would be an aggregate of the values pertaining to the expression landscape of different cell types. Therefore, many downstream analyses such as might be confounded by the variations in cell type proportions when using the output of high-throughput technologies applied to bulk samples. The development of statistical methods to identify cell type proportions in large-scale bulk samples is an important step for better understanding of the relationship between cell type composition and diseases. Cellular deconvolution algorithms have been applied to a variety of samples collected from saliva, buccal, cervical, PBMC, brain, kidney, and pancreatic cells, and many studies have shown that estimating and incorporating the proportions of cell types into various analyses improves the interpretability of high-throughput omics data and reduces the confounding effects of cellular , also known as tissue heterogeneity, in functional analysis of omics data, . (en)
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  • Cellular deconvolution (also referred to as cell type composition or cell proportion estimation) refers to computational techniques aiming at estimating the proportions of different cell types in samples collected from a tissue. For example, samples collected from the human brain are a mixture of various neuronal and glial cell types (e.g. microglia and astrocytes) in different proportions, where each cell type has a diverse gene expression profile. Since most high-throughput technologies use bulk samples and measure the aggregated levels of molecular information (e.g. expression levels of genes) for all cells in a sample, the measured values would be an aggregate of the values pertaining to the expression landscape of different cell types. Therefore, many downstream analyses such as migh (en)
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  • Cellular deconvolution (en)
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