Abstract
In this paper, we study the problem of computing a principal component analysis of data affected by Poisson noise. We assume samples are drawn from independent Poisson distributions. We want to estimate principal components of a fixed transformation of the latent Poisson means. Our motivating example is microbiome data, though the methods apply to many other situations. We develop a semiparametric approach to correct the bias of variance estimators, both for untransformed and transformed (with particular attention to log-transformation) Poisson means. Furthermore, we incorporate methods for correcting different exposure or sequencing depth in the data. In addition to identifying the principal components, we also address the nontrivial problem of computing the principal scores in this semiparametric framework. Most previous approaches tend to take a more parametric line: for example, fitting a log-normal Poisson (PLN) model. We compare our method with the PLN approach and find that in many cases our method is better at identifying the main principal components of the latent log-transformed Poisson means, and as a further major advantage, takes far less time to compute. Comparing methods on real and simulated data, we see that our method also appears to be more robust to outliers than the parametricmethod.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.