Perplexity: evaluating transcript abundance estimation in the absence of ground truth

dc.contributor.authorFan, Jason
dc.contributor.authorChan, Skylar
dc.contributor.authorPatro, Rob
dc.date.accessioned2022-07-25T13:57:59Z
dc.date.available2022-07-25T13:57:59Z
dc.date.issued2022-03-25
dc.description.abstractThere has been rapid development of probabilistic models and inference methods for transcript abundance estimation from RNA-seq data. These models aim to accurately estimate transcript-level abundances, to account for different biases in the measurement process, and even to assess uncertainty in resulting estimates that can be propagated to subsequent analyses. The assumed accuracy of the estimates inferred by such methods underpin gene expression based analysis routinely carried out in the lab. Although hyperparameter selection is known to affect the distributions of inferred abundances (e.g. producing smooth versus sparse estimates), strategies for performing model selection in experimental data have been addressed informally at best. We derive perplexity for evaluating abundance estimates on fragment sets directly. We adapt perplexity from the analogous metric used to evaluate language and topic models and extend the metric to carefully account for corner cases unique to RNA-seq. In experimental data, estimates with the best perplexity also best correlate with qPCR measurements. In simulated data, perplexity is well behaved and concordant with genome-wide measurements against ground truth and differential expression analysis. Furthermore, we demonstrate theoretically and experimentally that perplexity can be computed for arbitrary transcript abundance estimation models. Alongside the derivation and implementation of perplexity for transcript abundance estimation, our study is the first to make possible model selection for transcript abundance estimation on experimental data in the absence of ground truth.en_US
dc.description.urihttps://doi.org/10.1186/s13015-022-00214-y
dc.identifierhttps://doi.org/10.13016/xfag-oldz
dc.identifier.citationFan, J., Chan, S. & Patro, R. Perplexity: evaluating transcript abundance estimation in the absence of ground truth. Algorithms Mol Biol 17, 6 (2022).en_US
dc.identifier.urihttp://hdl.handle.net/1903/29069
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isAvailableAtCollege of Computer, Mathematical & Natural Sciencesen_us
dc.relation.isAvailableAtComputer Scienceen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectRNA-seqen_US
dc.subjectTranscript abundance estimationen_US
dc.subjectModel selectionen_US
dc.titlePerplexity: evaluating transcript abundance estimation in the absence of ground truthen_US
dc.typeArticleen_US

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