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For example, Chen and Mar generated simulated data to resemble scRNA-seq with a combined approach of GeneNetWeaver and added artificial dropout events to evaluate bulk-based, and single-cell-based GRN approaches [367]. Pratapa et al. introduced BEELINE as a single-cell transcriptome simulation framework leveraging Boolean network models [207]. Unlike GeneNetWeaver, BEELINE can simulate stochastic data with underlying cell trajectory, a hallmark feature in single-cell transcriptomes [207].
This study focuses on quantifying the extent of the third-order correlations for known-paired fMRI data, as well as defining the source of the third-order correlations as well. These results can lead to parameter estimation and sensitive testing of hypothesis about a third-order correlation model, which are difficult with the conventional F-statistic approach. The robustness of the test statistic is cross-checked with a test based on finite data sample.
Because single-cell data achieve comparable performance to RNA-seq by utilizing a fraction of the resources needed to generate bulk-based data sets, this research provides a framework for RNA-seq pipeline optimization using only scRNA-seq data. Because single-cell features may closely resemble bulk-based features, this work quantifies previously undefined bias due to cell type dilution. This information can be used to improve the extraction of cell-type specific information for downstream analysis. d2c66b5586