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.


The effect of zero removal on the model fit. Each Subfigure represents an individual sample. A proportion of zeros are removed (or added), and after refitting each model, we reassess model fit with the Total Variation distance dTV, plotted as −log(dTV) on the Y-axis against zero proportion on the X-axis. For severely zero-inflated data, deleting zeros should improve model fit, so we expect to see a “peak” somewhere to the right of the black vertical line. The NB and Poisson models recommend the removal of most of the zero-valued data, a conclusion that seems biologically implausible. In contrast, the NB-NB mixture consistently recommends either no zero-removal, or a modest level of zero-inflation in the case of sample A. Note the dramatic change in the NB-NB mixture's fit in sample D as additional counts are added—this could be due to the accelerated EM algorithm encountering alternative local maxima.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: The effect of zero removal on the model fit. Each Subfigure represents an individual sample. A proportion of zeros are removed (or added), and after refitting each model, we reassess model fit with the Total Variation distance dTV, plotted as −log(dTV) on the Y-axis against zero proportion on the X-axis. For severely zero-inflated data, deleting zeros should improve model fit, so we expect to see a “peak” somewhere to the right of the black vertical line. The NB and Poisson models recommend the removal of most of the zero-valued data, a conclusion that seems biologically implausible. In contrast, the NB-NB mixture consistently recommends either no zero-removal, or a modest level of zero-inflation in the case of sample A. Note the dramatic change in the NB-NB mixture's fit in sample D as additional counts are added—this could be due to the accelerated EM algorithm encountering alternative local maxima.

Mentions: The results are given in Figure 7. For sample A (dog) we see evidence of zero inflation, perhaps due to a less-established genome assembly. In samples B-D, the NB-NB models showed no evidence of zero-inflation. However, when using distributions that cannot account for the heavy tail in the high bin-counts, there is an erroneous indication that a zero-inflation component is necessary.


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

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

The effect of zero removal on the model fit. Each Subfigure represents an individual sample. A proportion of zeros are removed (or added), and after refitting each model, we reassess model fit with the Total Variation distance dTV, plotted as −log(dTV) on the Y-axis against zero proportion on the X-axis. For severely zero-inflated data, deleting zeros should improve model fit, so we expect to see a “peak” somewhere to the right of the black vertical line. The NB and Poisson models recommend the removal of most of the zero-valued data, a conclusion that seems biologically implausible. In contrast, the NB-NB mixture consistently recommends either no zero-removal, or a modest level of zero-inflation in the case of sample A. Note the dramatic change in the NB-NB mixture's fit in sample D as additional counts are added—this could be due to the accelerated EM algorithm encountering alternative local maxima.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 7: The effect of zero removal on the model fit. Each Subfigure represents an individual sample. A proportion of zeros are removed (or added), and after refitting each model, we reassess model fit with the Total Variation distance dTV, plotted as −log(dTV) on the Y-axis against zero proportion on the X-axis. For severely zero-inflated data, deleting zeros should improve model fit, so we expect to see a “peak” somewhere to the right of the black vertical line. The NB and Poisson models recommend the removal of most of the zero-valued data, a conclusion that seems biologically implausible. In contrast, the NB-NB mixture consistently recommends either no zero-removal, or a modest level of zero-inflation in the case of sample A. Note the dramatic change in the NB-NB mixture's fit in sample D as additional counts are added—this could be due to the accelerated EM algorithm encountering alternative local maxima.
Mentions: The results are given in Figure 7. For sample A (dog) we see evidence of zero inflation, perhaps due to a less-established genome assembly. In samples B-D, the NB-NB models showed no evidence of zero-inflation. However, when using distributions that cannot account for the heavy tail in the high bin-counts, there is an erroneous indication that a zero-inflation component is necessary.

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.