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

Jason Fan¹, Skylar Chan², Rob Patro³

¹Center for Bioinformatics and Computational Biology, University of Maryland, College Park, USA. jasonfan@umd.edu.

Algorithms for Molecular Biology : AMB

|March 25, 2022

Summary

We introduce perplexity, a novel metric for evaluating RNA-seq transcript abundance estimates. This method enables model selection on experimental data without ground truth, improving gene expression analysis accuracy.

Keywords:

Model selection RNA-seq Transcript abundance estimation

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA-sequencing (RNA-seq) enables transcript abundance estimation.
Probabilistic models and inference methods are crucial for accurate transcript abundance estimation.
Model selection for these methods in experimental data is currently informal.

Purpose of the Study:

To derive and validate perplexity as a metric for evaluating transcript abundance estimates from RNA-seq data.
To enable formal model selection for transcript abundance estimation using experimental data.
To assess the performance of perplexity in both simulated and experimental datasets.

Main Methods:

Adapted perplexity from language and topic modeling for RNA-seq data.
Extended perplexity to handle RNA-seq specific challenges.
Evaluated perplexity against qPCR measurements and genome-wide data.

Main Results:

Perplexity effectively evaluates transcript abundance estimates on fragment sets.
Estimates with optimal perplexity correlate strongly with qPCR measurements in experimental data.
Perplexity is well-behaved in simulated data, aligning with ground truth and differential expression analysis.

Conclusions:

Perplexity provides a robust method for evaluating transcript abundance estimation models.
This study enables formal model selection for RNA-seq data analysis without requiring ground truth.
Perplexity is theoretically and experimentally shown to be applicable to arbitrary transcript abundance estimation models.