Negative Binomial Approximation to the Beta Binomial Distribution

Teerapabolarn, K.

doi:10.12732/ijpam.v98i1.5

Cited by 6 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, we opted to implement a Negative Binomial NMF model for mutational count data. More details on the approximation of the Negative Binomial to the Beta-Binomial distribution can also be found in [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

2023

View full text Add to dashboard Cite

Background The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, and the rank is chosen by comparing the fit of several models with the same underlying distribution and different values for the rank using classical model selection procedures. However, the counts are often overdispersed, and thus the Negative Binomial distribution is more appropriate. Results We propose a Negative Binomial NMF with a patient specific dispersion parameter to capture the variation across patients and derive the corresponding update rules for parameter estimation. We also introduce a novel model selection procedure inspired by cross-validation to determine the number of signatures. Using simulations, we study the influence of the distributional assumption on our method together with other classical model selection procedures. We also present a simulation study with a method comparison where we show that state-of-the-art methods are highly overestimating the number of signatures when overdispersion is present. We apply our proposed analysis on a wide range of simulated data and on two real data sets from breast and prostate cancer patients. On the real data we describe a residual analysis to investigate and validate the model choice. Conclusions With our results on simulated and real data we show that our model selection procedure is more robust at determining the correct number of signatures under model misspecification. We also show that our model selection procedure is more accurate than the available methods in the literature for finding the true number of signatures. Lastly, the residual analysis clearly emphasizes the overdispersion in the mutational count data. The code for our model selection procedure and Negative Binomial NMF is available in the R package SigMoS and can be found at https://github.com/MartaPelizzola/SigMoS.

show abstract

Section: Methodsmentioning

confidence: 99%

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

2023

View full text Add to dashboard Cite

show abstract

“…Given this set-up, when t > 1, the marginal predictive conditional distribution describing expression (or emissions) for each state is Beta-NB:

where

. The expected mean is then defined as

( Teerapabolarn, 2008 ). When t = 1, the prior distribution for

is defined as

for all g , and the marginal predictive distribution is

.…”

Section: Methodsmentioning

confidence: 99%

EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

Leng

McIntosh

et al. 2015

Bioinformatics

View full text Add to dashboard Cite

Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data.Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression.Availability and implementation: An R package containing examples and sample datasets is available at Bioconductor.Contact: kendzior@biostat.wisc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

show abstract

“…Thus, we opted to implement a NB-NMF model for mutational count data. More details on the approximation of the Negative Binomial to the Beta-Binomial distribution can also be found in Teerapabolarn (2015).…”

Section: Negative Binomial Model For Mutational Countsmentioning

confidence: 99%

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

Pelizzola¹,

Ragnhild²,

Hobolth³

2022

Preprint

View full text Add to dashboard Cite

The spectrum of mutations in a collection of cancer genomes can be described by a mixture of a few mutational signatures. The mutational signatures can be found using non-negative matrix factorization (NMF). To extract the mutational signatures we have to assume a distribution for the observed mutational counts and a number of mutational signatures. In most applications, the mutational counts are assumed to be Poisson distributed, but they are often overdispersed, and thus the Negative Binomial distribution is more appropriate. We demonstrate using a simulation study that Negative Binomial NMF requires fewer signatures than Poisson NMF to fit the data and we propose a Negative Binomial NMF with a patient specific overdispersion parameter to capture the variation across patients. We also introduce a robust model selection procedure inspired by cross-validation to determine the number of signatures. Furthermore we study the influence of the distributional assumption in relation to two classical model selection procedures: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). In the presence of overdispersion we show that our model selection procedure is more robust at determining the correct number of signatures than state-of-the-art methods, which are overestimating the number of signatures. We apply our proposed analysis on a wide range of simulated data and on a data set from breast cancer patients. The code for our algorithms and analysis is available in the R package SigMoS and can be found at https://github.com/ MartaPelizzola/SigMoS.

show abstract

Negative Binomial Approximation to the Beta Binomial Distribution

Cited by 6 publications

References 3 publications

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization

Contact Info

Product

Resources

About