Limits...
Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data.

Cairns J, Lynch AG, Tavaré S - Front Genet (2014)

Bottom Line: The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data.Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model.Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution.

View Article: PubMed Central - PubMed

Affiliation: Nuclear Dynamics Group, The Babraham Institute Cambridge, UK ; Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK.

ABSTRACT
Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) is a valuable tool for epigenetic studies. Analysis of the data arising from ChIP-seq experiments often requires implicit or explicit statistical modeling of the read counts. The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data. Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model. Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution. We apply the Constrained Newton Method (CNM), which suggests the Negative Binomial - Negative Binomial (NB-NB) mixture model as a candidate for modeling ChIP-seq data. We illustrate fitting the NB-NB model with an accelerated EM algorithm on four data sets from three species. Zero-inflated models have been suggested as an approach to improve model fit for ChIP-seq data. We show that the NB-NB mixture model requires no zero-inflation and suggest that in some cases the need for zero inflation is driven by the model's inability to cope with both artifactual large read counts and the frequently observed very low read counts. We see that the CNM-based approach is a useful diagnostic for the assessment of model fit and inference in ChIP-seq data and beyond. Use of the suggested NB-NB mixture model will be of value not only when calling peaks or otherwise modeling ChIP-seq data, but also when simulating data or constructing blacklists de novo.

No MeSH data available.


Predicted mixing distributions, as per Figure 3, for each sample with the NB-NB mixture added. (Sample D is omitted since CNM did not recover its distribution.) The NB-NB mixture is the only model whose mixing distribution consistently fits between the CNM bounds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4231950&req=5

Figure 6: Predicted mixing distributions, as per Figure 3, for each sample with the NB-NB mixture added. (Sample D is omitted since CNM did not recover its distribution.) The NB-NB mixture is the only model whose mixing distribution consistently fits between the CNM bounds.

Mentions: Then we examine the similarity between the mixing distributions used and the observed mixing distribution as calculated by CNM, in Figure 6.


Quantifying the impact of inter-site heterogeneity on the distribution of ChIP-seq data.

Cairns J, Lynch AG, Tavaré S - Front Genet (2014)

Predicted mixing distributions, as per Figure 3, for each sample with the NB-NB mixture added. (Sample D is omitted since CNM did not recover its distribution.) The NB-NB mixture is the only model whose mixing distribution consistently fits between the CNM bounds.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4231950&req=5

Figure 6: Predicted mixing distributions, as per Figure 3, for each sample with the NB-NB mixture added. (Sample D is omitted since CNM did not recover its distribution.) The NB-NB mixture is the only model whose mixing distribution consistently fits between the CNM bounds.
Mentions: Then we examine the similarity between the mixing distributions used and the observed mixing distribution as calculated by CNM, in Figure 6.

Bottom Line: The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data.Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model.Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution.

View Article: PubMed Central - PubMed

Affiliation: Nuclear Dynamics Group, The Babraham Institute Cambridge, UK ; Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK.

ABSTRACT
Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) is a valuable tool for epigenetic studies. Analysis of the data arising from ChIP-seq experiments often requires implicit or explicit statistical modeling of the read counts. The simple Poisson model is attractive, but does not provide a good fit to observed ChIP-seq data. Researchers therefore often either extend to a more general model (e.g., the Negative Binomial), and/or exclude regions of the genome that do not conform to the model. Since many modeling strategies employed for ChIP-seq data reduce to fitting a mixture of Poisson distributions, we explore the problem of inferring the optimal mixing distribution. We apply the Constrained Newton Method (CNM), which suggests the Negative Binomial - Negative Binomial (NB-NB) mixture model as a candidate for modeling ChIP-seq data. We illustrate fitting the NB-NB model with an accelerated EM algorithm on four data sets from three species. Zero-inflated models have been suggested as an approach to improve model fit for ChIP-seq data. We show that the NB-NB mixture model requires no zero-inflation and suggest that in some cases the need for zero inflation is driven by the model's inability to cope with both artifactual large read counts and the frequently observed very low read counts. We see that the CNM-based approach is a useful diagnostic for the assessment of model fit and inference in ChIP-seq data and beyond. Use of the suggested NB-NB mixture model will be of value not only when calling peaks or otherwise modeling ChIP-seq data, but also when simulating data or constructing blacklists de novo.

No MeSH data available.