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A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

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Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions. |

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