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Consequently, these methods are not evaluated for expression path classification.
The ground truth shows the number of genes simulated in each expression path.
Recall that EBSeq-HMM provides gene-specific posterior probabilities associated with each expression path.
For EBSeq-HMM, a DE gene is classified into a specific expression path if its PP of being in that path exceeds 0.5.
Two tasks are of interest: identifying DE genes, defined as those showing any change across conditions; and assigning DE genes into their most likely expression path.
Taken together, since we do not know the expression path type a priori, the model for the full set of expression measurements is a two-component mixture over the sporadic/constant and dynamic genes.
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As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths.
Figure 4 shows expression levels of the 33 genes along with their most likely expression paths.
EBSeq-HMM also provides improved accuracy in classifying genes into expression paths.
In addition to DE gene identification, EBSeq-HMM performed well for classifying genes into expression paths.
EBSeq-HMM allows users to identify genes with non-constant expression over multiple ordered conditions, and simultaneously classify them into expression paths.
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CEO of Professional Science Editing for Scientists @ prosciediting.com