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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.


Candidate mixing distributions, and their consistency with the CNM-derived mixing distribution. For clarity, we plot log(1 − CDF) where CDF is the Cumulative Density Function of Λ, and values are calculated only at CNM's λ support points. A mixing distribution that is consistent with CNM's predicted mixing distribution, as derived in Section 3.2, would have a line contained within the shaded region, with the black lines representing upper and lower bounds. Here, the lines do not stay within the bounds, indicating that all of the models deviate from CNM's prediction.
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Figure 3: Candidate mixing distributions, and their consistency with the CNM-derived mixing distribution. For clarity, we plot log(1 − CDF) where CDF is the Cumulative Density Function of Λ, and values are calculated only at CNM's λ support points. A mixing distribution that is consistent with CNM's predicted mixing distribution, as derived in Section 3.2, would have a line contained within the shaded region, with the black lines representing upper and lower bounds. Here, the lines do not stay within the bounds, indicating that all of the models deviate from CNM's prediction.

Mentions: Figure 3 shows an example of this procedure, as applied to sample A. We see that all of the candidate mixing distributions considered thus far violate the CNM bounds early on, indicating that these distributions cannot cope with large counts. This motivates the selection of a mixing distribution that can stay within the bounds.


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

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

Candidate mixing distributions, and their consistency with the CNM-derived mixing distribution. For clarity, we plot log(1 − CDF) where CDF is the Cumulative Density Function of Λ, and values are calculated only at CNM's λ support points. A mixing distribution that is consistent with CNM's predicted mixing distribution, as derived in Section 3.2, would have a line contained within the shaded region, with the black lines representing upper and lower bounds. Here, the lines do not stay within the bounds, indicating that all of the models deviate from CNM's prediction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Candidate mixing distributions, and their consistency with the CNM-derived mixing distribution. For clarity, we plot log(1 − CDF) where CDF is the Cumulative Density Function of Λ, and values are calculated only at CNM's λ support points. A mixing distribution that is consistent with CNM's predicted mixing distribution, as derived in Section 3.2, would have a line contained within the shaded region, with the black lines representing upper and lower bounds. Here, the lines do not stay within the bounds, indicating that all of the models deviate from CNM's prediction.
Mentions: Figure 3 shows an example of this procedure, as applied to sample A. We see that all of the candidate mixing distributions considered thus far violate the CNM bounds early on, indicating that these distributions cannot cope with large counts. This motivates the selection of a mixing distribution that can stay within the bounds.

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.