CellNet is such a cell fate assessment tool that utilizes network biology to both evaluate and suggest candidate transcriptional regulatory modifications to improve the similarity of an engineered population to its corresponding in vivo target population.  CellNet takes in expression profiles in the form of RNA-sequencing data and generates several metrics of cell identity and protocol efficacy.  In this chapter, we demonstrate how to (1) preprocess raw RNA-sequencing data to generate an expression matrix, (2) train CellNet using preprocessed expression matrices, and (3) apply CellNet to a query study and interpret its results.  We demonstrate the utility of CellNet for analysis of iPSC disease modeling studies, which we evaluate through the lens of cell fate engineering.
CellNet
CellNet is such a cell fate assessment tool that utilizes network biology to both evaluate and suggest candidate transcriptional regulatory modifications to improve the similarity of an engineered population to its corresponding in vivo target population.  CellNet takes in expression profiles in the form of RNA-sequencing data and generates several metrics of cell identity and protocol efficacy.  In this chapter, we demonstrate how to (1) preprocess raw RNA-sequencing data to generate an expression matrix, (2) train CellNet using preprocessed expression matrices, and (3) apply CellNet to a query study and interpret its results.  We demonstrate the utility of CellNet for analysis of iPSC disease modeling studies, which we evaluate through the lens of cell fate engineering.
CellNet
CellNet is such a cell fate assessment tool that utilizes network biology to both evaluate and suggest candidate transcriptional regulatory modifications to improve the similarity of an engineered population to its corresponding in vivo target population.  CellNet takes in expression profiles in the form of RNA-sequencing data and generates several metrics of cell identity and protocol efficacy.  In this chapter, we demonstrate how to (1) preprocess raw RNA-sequencing data to generate an expression matrix, (2) train CellNet using preprocessed expression matrices, and (3) apply CellNet to a query study and interpret its results.  We demonstrate the utility of CellNet for analysis of iPSC disease modeling studies, which we evaluate through the lens of cell fate engineering.
CellNet