Abstract
Background
Hidden Markov Models (HMM) are often used for analyzing Comparative Genomic Hybridization (CGH) data to identify chromosomal aberrations or copy number variations by segmenting observation sequences. For efficiency reasons the parameters of a HMM are often estimated with maximum likelihood and a segmentation is obtained with the Viterbi algorithm. This introduces considerable uncertainty in the segmentation, which can be avoided with Bayesian approaches integrating out parameters using Markov Chain Monte Carlo (MCMC) sampling. While the advantages of Bayesian approaches have been clearly demonstrated, the likelihood based approaches are still preferred in practice for their lower running times; datasets coming from highdensity arrays and next generation sequencing amplify these problems.
Results
We propose an approximate sampling technique, inspired by compression of discrete sequences in HMM computations and by kdtrees to leverage spatial relations between data points in typical data sets, to speed up the MCMC sampling.
Conclusions
We test our approximate sampling method on simulated and biological ArrayCGH datasets and highdensity SNP arrays, and demonstrate a speedup of 10 to 60 respectively 90 while achieving competitive results with the stateofthe art Bayesian approaches.
Availability: An implementation of our method will be made available as part of the open source GHMM library from http://ghmm.org webcite.
Background
The Sirens' call of Bayesian methods is that we can do without the parameters of models and, instead, compute probabilities of interest directly, indicating for example how likely a biological fact is given our data. The price one pays for that convenience is on one hand the conundrum of which prior distributions to choose and how to set their hyperparameters; the frequent use of uniform priors is evidence that this is indeed nontrivial. On the other hand, unless the choice of likelihood and prior yields simple posteriors which we can integrate symbolically, we have to resort to sampling for example with Markov Chain Monte Carlo (MCMC) [1].
In the following we will concentrate on HMMs, stochastic functions of Markov Chains, which have not only been used extensively for discrete sequencespairwisesequence alignments with pairHMMs [2], gene finding with labeled HMMs [3], and detecting remote homologs using profile HMMs [4]but also for continuousvalued observations, such as gene expression timecourses [5]. Continuous observation sequences from either DNA microarrays or next generation sequencing experiments, note that the proportion of mapped reads in an interval is frequently used as a continuous measure of copy number, to detect chromosomal aberrations or copy number variations lead to the same fundamental computational problem and share characteristics of the data. The goal is to segment an observation sequence into regions in which there is little variation around a common mean. In other words, the assumption is that the data can be approximately described by piecewise constant functions. Indeed, if hybridization intensity was an exact, unbiased measurement of DNA concentration before amplification, the sequence of hybridization intensities of probes along a chromosome would yield a piecewise constant function in ArrayCGH experiments. This assumption holds true for a mixture of different cell populations because a finite sum of piecewise constant functions is again a piecewise constant function.
A wide range of methods for copy number detection in ArrayCGH data have been developed in recent years, including changepoint detection based methods [6,7], smoothing based methods [8,9], and hierarchical clustering [10]. Here, we concentrate on HMMbased approaches which have been proposed for segmenting sequences of continuousvalued observations and shown to match or improve upon the stateoftheart [1113]. Once a model is trained from the data, either using maximum likelihood (ML) or maximum a posteriori (MAP), the segmentation is given by the most likely state sequence obtained with the Viterbi algorithm [14]. The segmentation, however, is highly dependent on the model parameters. A small change in the computed parameter values can adversely affect the recovered segmentation. A full Bayesian approach alleviates this dependence by integrating out the model parameters. As analytic integration of a complex high dimensional model is impossible for most distributions, the Bayesian approach requires the use of numerical integration techniques like MCMC [15], for example by direct Gibbs sampling [16] of model parameters and state paths. Scott [17] reports that combining the forwardbackward recursions [18] and Gibbs sampling improves the converge rate and consequently the running time. Nevertheless, MCMC remains substantially slower than training one model and running Viterbi once and the loss in reliability introduced by relying on one ML or MAP model is ignored in practice. For discrete emissions, compressing sequences and computing forward and backward variables and Viterbi paths on the compressed sequences yields impressive speedups [19]. However, discretization of continuous emissions, similar to vector quantization used in speech recognition [18], is not viable as the separation between the different classes of observations is low since the observations are onedimensional. Moreover, maximal compression is to be expected for small number of discrete symbols and clearly compression ratio conflicts with fidelity in the analysis.
For a different task, arguments about spatial relations between groups of multivariate data points were used to achieve considerable speedup. Moore and colleagues used modified kdtrees, a data structure to efficiently execute spatial queries such as determining the nearest neighbor of a given point, to accelerate kmeans [20]. The fundamental insight is illustrated in Figure 1 (left). In the reassignment step of kmeans one has to find the nearest centroid for every data point. Due to the kdtree, there are groups of points contained in a node of the tree for which this decision about the nearest centroid can be made simultaneously by a geometrical argument about the vertices of the hyperrectangle defined by this node. A similar kdtree based approach was used in speech recognition [21,22] to quickly find the most important components in a mixture of large number of Gaussians and thus approximate the full observation density in one individual HMM state with multivariate emissions.
Figure 1. Fundamental insight. When reassigning twodimensional data points to cluster centroids c and d in kmeans clustering (left) the hyperrectangles obtained from kdtrees reduce the computational effort by making an argument about all points in an hyperrectangle based on their vertices; consider for example the rightmost hyperrectangle. For sequences (middle) there is no overlap in ydirection and decisions about the most likely state can be made per block considering the means of the Gaussians of a threestate HMM (right), μ_{}, μ_{= }and μ_{+}. Note that at every given block only a decision between the two states with closest mean is necessary, if one assume comparable variances. Decision boundaries are displayed dashed.
At the core of our approach is a similar geometrical argument about several univariate data points based on a modified kdtree. We adaptively identify blocks of observations, cf. Figure 1 (middle). For all observations in a block we now estimate, at least conceptually, the most likely state simultaneously depending on the means of the Gaussians in each state to gain a considerable speedup proportional to the average block length. Similarly, we can avoid sampling states for each individual observation in a block if we can bound the posterior. Considerable care has to be taken for combining blocks and to bound the errors introduced by the approximations based on geometric arguments.
In summary, we
• propose the first use of spatial index structures for several consecutive observations and approximate computations based on geometric arguments to substantially speedup the problem of segmenting sequences of continuous observations using HMM,
• demonstrate that very frequently used biological datasets of high relevance measuring chromosomal aberration and copy number variations are consistent with our assumptions of piecewise constant observations with added noise, and
• achieve speedups between 10 and 90 on those biological datasets while maintaining competitive performance compared to the stateoftheart.
Methods
HMM
We only consider HMMs with Gaussian emission distributions; see Figure 1 (right) for an example and [18] for an introduction. We will use the following notation: N denotes the number of states, S = {s_{1}, s_{2}, ..., s_{N}} the set of states, T the length of an observation sequence O = {o_{1}, o_{2}, ..., o_{T}} with o_{t }∈ ℝ, A = {a_{ij}}_{1≤i,j≤N }the transition matrix, π = (π_{1}, π_{2}, ..., π_{N}) the initial distribution over states,
MCMC Sampling
Bayesian analysis requires choosing prior distributions on parameters and we use standard
conjugate prior distributions following [1]. Specifically, we choose
It is only possible in some trivial cases to compute posterior distribution in closed form using analytic integration. In most applications, specially for high dimensional distributions, Monte Carlo integration techniques, like MCMC sampling by Gibbs sampling or MetropolisHastings, are easier to compute and generally produce more accurate results than numerical integration [15]. Scott [17] compares various MCMC approaches and strongly argues in favor of forwardbackward Gibbs sampling (FBGsampling), which has been successfully used by others [24,25], for it's excellent convergence characteristics. We briefly summarize FBGsampling for an HMM λ = (A, B, π); see [17,26] for details:
1. Choose initial parameters θ^{0 }= (A^{0}, B^{0}, π^{0})
2. Perform the following steps for iteration 0 ≤ m < M.
(a) Compute forward variables P(q_{t }= i, O_{1, ...,t}θ^{m}) for 1 ≤ t ≤ T iteratively using the forward algorithm [18] for all i.
(b) Sample
(c) Sample the state sequence Q^{m }in a backward fashion for T > t ≥ 1.
(d) Sample,
Despite its fast convergence, FBGsampling takes O(MTN^{2}) time for M iterations. For long observation sequences with millions of observations, as they are common in biological applications, realistic values for M and N make FGBsampling intractable. In the next section we discuss how FBGsampling can be approximated to improve the running time to O(γMTN^{2}), where γ is the compression ratio of the observation sequence, while maintaining good statistical properties. We refer to our sampling technique as approximate sampling.
Approximate sampling
Through application of a modified kdtree algorithm (details below), we compress the
observation sequence O = o_{1}, ..., o_{T }into
At first ignoring weak state paths may sound like a very crude approximation for computing
forward variables. But in many applications this is certainly not true. We demonstrate with a symmetric
Gaussian HMM that the weak state path assumption is a fairly realistic approximation and leads to faster sampling. We define
a symmetric HMM λ = (A, B, π) with N states s_{1}, ..., s_{N}, where we set selftransition probability a_{ii }= t and nonselftransition probability
Lemma 1. Let
Proof. See Appendix.
Remark 1 For realistic values of τ, t, and n, the contribution from ignored weak state paths, which we call ϵ, can be very small. If ϵ ≪ 1, ignoring weak state paths will not introduce large errors in the computation.
For the 2state example in Section: Results, where t = 0.9, d = 1, and σ^{2 }= 0.1, ϵ is at most
Remark 2 We often encounter situations where P(q_{i }= s_{x}O^{i}^{1}) ≫ P(q_{i }≠ s_{x}O^{i}^{1}). Even though it is not exploited in the lemma (α being greater than or equal to 1), as a consequence of this, the observation sequence can be compressed into larger blocks keeping ϵ small in practice.
From the above lemma and the remarks we see that the longer blocks created by an algorithm should concentrate around the means of the Gaussian distributions. While a brute force algorithm looks at local information, a kdtree like algorithm alternately looks at both dimensions and utilizes global information like the density of data points (around the means data concentration should be high) to create better quality blocks. We use a modified kdtree based algorithm to find such blocks and discuss the details below.
kdtree Based Sequence Compression
Given a starting width parameter w we create a list of nodes from the observation sequence O = o_{1}, ..., o_{T }using the following steps.
1. Let O' = ϕ be the starting list, δ = 1.25 (picked empirically), level L = 1, and dimension d = 1.
2. If
3. If d = 1, find o_{m}, the median value of the observations in O. Partition O into maximal set of consecutive observations O^{1}, ..., O^{i}, ..., O^{p }such that
4. If d = 0, divide the input set O into two parts O^{l }= o_{1}, ..., o_{i }and O^{r }= o_{i+1}, ..., o_{O }such that
5. End step.
In the above recursive algorithm, w states the initial width, δ controls the rate of width shrinking in successive levels of the iterations, and O' accumulates the compressed blocks of observations. The current iteration level L, the current dimension d, and the current input set O are local variables in the recursive algorithm. Notice that we start with an empty list O' and at the end of the recursive procedure O' contains an ordered list of compressed observations. To gain further compression of the sequence, we sequentially go through the blocks of O' and combine consecutive blocks if the distance between their means is less than w. We also combine three consecutive blocks if the outer blocks satisfy this condition and the inner block has only one observation. In step 3 of the above algorithm, the input set is divided into two subsets and each subset contains half of the elements from the original set. Consequently, the height of the recursion tree is at most 2logT and the running time of the above algorithm is O(T log T). This overhead is negligible compared to the time that it takes to run M iterations of MCMC sampling.
Width Parameter Selection
For increasing values of w the average block size increases exponentially in the above kdtree based compression. As a result, the compression ratio
Fast Approximate Sampling
Given the compressed input sequence
1. Modified forward variables at positions
2. After sampling the state sequence, parameters are updated ignoring nonself transitions
between consecutive observations in
Clearly, each iteration of approximate sampling takes O(T' N^{2}) resulting in
Results
We evaluate FBGsampling and approximate sampling in three different settings. First,
its effectiveness is verified for a simple two state model. Then, we test on simulated
ArrayCGH data which is the accepted standard for method evaluation [28]. Finally, we report findings from an analysis of Mantle Cell Lymphoma (MCL) cell
lines [29], Corriel cell lines [30], GBM datasets [31], and high resolution SNP arrays [13,32]. For biological data, if multiple chromosomes are present, we use pooling [25] across chromosomes, which does not allow transition between different chromosomes
but assumes model parameters to be identical across chromosomes. Throughout this section
we define σ_{D }to be the standard deviation of all observations in the dataset. We compress the dataset
with increasing values of w = 0.25σ_{D}, 0.5σ_{D}, 0.75σ_{D}, .... For evaluation we consider the experiments as two class problems: aberrant
clones belong to the positive class and normal clones belong to the negative class.
When ground truth labels of a dataset are available we report F1measure, recall,
and precision for the experiment. With tp, fp, tn, fn we denote the number of true and false positives and true and false negatives respectively.
Recall is defined as
Synthetic Data
2State HMM
We define a HMM λ_{2}_{ST }= (A, B, π) with
After M iterations, we compare the posterior probabilities
Figure 2. Simulated data: approximate posterior. We show the posterior probability of state 1 (yaxis) for first fifty observations (xaxis) with w = 0.5σ_{D }(top left), 1.0σ_{D }(top right), 1.5σ_{D }(bottom left), and 2.0σ_{D }(bottom right). The true posterior is shown as a solid line, the approximate posterior as a dashed line, and their absolute difference is shown in dashed vertical lines.
Table 1. We show the average posterior error
Simulation from Genetic Template
We use 500 simulated datasets published in [28]. Each dataset has 20 chromosomes and 100 clones per chromosome for a total of 2,000
clones per dataset. A fourstate HMM predicts the aberrant regionsloss defined as
state S_{1 }and gain defined as state S_{3 }or S_{4}. The neutral region is modeled as state S_{2}. We put an ordering constraint on the means, μ_{1 }< μ_{2 }< μ_{3 }< μ_{4}, to prevent label switching of the states [17]. Hyperparameter choices follow [25] and are
Table 2 shows the mean and standard deviation of F1measure, recall, and precision over the 500 datasets for FBGsampling, approximate sampling, and Expectation Maximization (EM) with the ground truth provided by [28]. Even for this collection of relatively small datasets we see a 10fold speed up. For each dataset we run FBG and approximate sampling for M = 100 steps (we have visually monitored the parameters and noticed convergence within 50 steps, see Figure 3 for a representative example). The last 10 samples are used to compute 10 samples of the posteriors for each state and for each position in the observation sequence. Subsequently, aberrant regions are predicted based on the average of those distributions. We report the speedup of approximate vs. FBG sampling based on the time it takes to compress the sequence and run M steps of MCMC. For one individual dataset EM requires 58 seconds on average, which allows for a total of 8001000 repetitions from randomized points sampled from the prior distributions in the time needed for FBG sampling. Each run continues until the likelihood converges and the best model based on likelihood is selected. Aberrant regions are predicted and compared against the ground truth based on the Viterbi path. We report the mean and standard deviation of F1measure, recall, and precision over the results of EM on 500 datasets.
Table 2. EM, FBGsampling, and approximate sampling results for simulated, HBL2, and Corriel dataset are shown here
Figure 3. MCMC convergence. The convergence of posterior probabilities for loss, neutral, and gain of three representative probesprobe 1658, probe 1512, and probe 447 respectivelyfrom the simulated dataset 63 are shown. For each probe, at first, the posterior probability of the corresponding HMM state, given the sampled parameters from the current MCMC iteration, is computed. The timeaverage of these posterior probabilities, starting from the first iteration to the current iteration, approximates the posterior of the HMM state given the data. The mean of the posterior probabilities over 10 MCMC chains are shown with error bars (mean ± one standard deviation)loss probe in the bottom row, neutral probe in the middle, and the gain probe in the top row. The top figures show the outcomes of FBG sampling and the bottom figures show the outcomes of approximate sampling. Note that the reduction in standard deviation suggests that approximate sampling converges quicker than FBG sampling for these probes.
Biological Data
Mantle Cell Lymphoma (MCL)
De Leeuw and colleagues identified recurrent variations across cell lines using ArrayCGH
data of MCL cell lines [29]. Out of the eight cell lines [29] HBL2 was fully annotated with marked gain and loss regions in the autosomes. This
dataset contains about 30,000 data points (combining all the autosomes). We have used
a fourstate HMM for predicting aberrant regions. State 1 represents copy number loss,
state 2 represents normal copy number, state 3 represents single copy gain, and state
4 multiple gain. For HBL2 we report the F1measure, recall, precision and speedup.
Similar to the synthetic case we put an ordering constraint on the means, μ_{1 }< μ_{2 }< μ_{3 }< μ_{4}. Hyperparameter choices follow [25] and are same as for the simulation from genetic template, except for
Figure 4. HBL2: chromosome 1 and 9. We contrast the ground truth and the segmentations produced by FBGsampling and approximate sampling. For approximate sampling w was set to the value recommended by the Lmethod. Here, clones predicted as loss are shown in red, normal clones in green, and gain in blue. The figure at the top shows chromosome 1 and the bottom figure shows chromosome 9.
Corriel
Corriel cell lines were used by Snijders et al. [30] and are widely considered a gold standard in ArrayCGH data analysis. This dataset is smaller and, in fact, fairly easy compared to the MCL cell lines. For the Corriel cell line we use a 4state HMM and report the results for GM05296 and GM00143 in Table 2. Again, approximate sampling performs competitively while achieving more than a 10fold speedup. Hyperparameter choices follow [24].
GBM
The glioma data from Bredel et al. [31] has previously been used to analyze the performance of CNV detection methods [9,33]. According to [33], GBM datasets are noisy but contains a mixture of aberrant regions with different
width and amplitude. In particular, chromosome 13 of GBM31 is reported to have low
amplitude loss in parm and chromosome 7 of GBM29 is reported to have high amplitude
gains near the EGFR locus by previous studies [9,33]. The segmentation of these two chromosomes are shown in Figure 5. Although [33] reports that EM based HMM failed to detect these aberrations we see that Bayesian
HMM has successfully detected both the gain in chromosome 7 and the loss in chromosome
13. For this dataset, we use a 3state HMM with noninformative hyperparameters,
Figure 5. GBM: chromosome 7 (GBM29) and chromosome 13 (GBM31). Loss (red), normal (green), and gain (blue) clones are identified using FBGsampling and approximate sampling. For approximate sampling w = 1.5σ_{D }is used, which was recommended by the Lmethod.
SNP Array
Highresolution Single Nucleotide Polymorphism (SNP) arrays are capable of detecting smaller CNVs than ArrayCGH. To demonstrate the computational advantage of approximate sampling on SNP arrays we have chosen publicly available Affymetrix 100 k pancreatic cancer datasets from [32] and Illumina HumanHap550 arrays of HapMap individuals which are provided as examples in PennCNV [13]. An Affymetrix 100 k dataset consists of two different arrays each with ≈ 60, 000 SNP markers and, in total, 10^{5 }data points per sample. On the other hand, the Illumina HumanHap550 array has around half a million SNP markers. We have applied FBGsampling and approximate sampling with w = 1.8σ_{D}, the recommended value by the Lmethod, to the sample datasets from Harada et al. [32] and found that the computational speedup is 22fold (100 runs of FBGsampling takes 1844 seconds). Both sampling approaches mostly agree on their predictions but they, specially FBGsampling, detect several more CNVs than previously identified [32]. For example, the high amplification in chromosome 11 (sample 33) is successfully identified by all methods but in chromosome 18 (sample 16) the sampling algorithms find a few normal regions previously predicted [32] as loss using the CNAG tool [34] (see Figure 6). One possible reason for these differences is that while we use 269 HapMap samples as reference they use 12 unpublished normal samples as reference. Similarly, we have tested our method with 2.0σ_{D }≤ w ≤ 3.0σ_{D }against Illumina HumanHap samples and observed 70 to 90 fold speedup in computations (100 runs of FBGsampling takes 9693 seconds). These samples are taken from apparently healthy individuals and contain very few CNVs. As expected, both sampling algorithms' predictions are nearly identical and they seem to predict extreme outliers as aberrant markers. In contrast, PennCNV [13] does not report CNVs which are covered by less than 3 SNPs, thus suppressing the outliers as normal. We plot a typical region (from 1.4e + 08bp to 1.7e + 08bp) of chromosome 6 from sample 3 (ID 99HI0700A) in Figure 7.
Figure 6. Affymetrix 100 k SNP array: chromosome 18 of sample 16 and chromosome 11 of sample 33. Loss (red), normal (green), and gain (blue) clones are identified using FBGsampling and approximate sampling. For approximate sampling w = 1.8σ_{D }is used, which was recommended by the Lmethod.
Figure 7. Illumina HumanMap550 array: chromosome 6 of sample 3. Loss (red), normal (green), and gain (blue) clones are identified using FBGsampling and approximate sampling. For approximate sampling w = 1.6σ_{D }is used, which was recommended by the Lmethod.
To set hyperparameters we follow the default parameters of the HMM used in PennCNV
[13]. We have observed that HMMs for large arrays are particularly sensitive to the selftransition
probabilities (which is also reflected in the default parameter values of the HMM
used in PennCNV). Hence, hyperparameters were set to reflect the choice of high selftransition
probability for each statewe set
Discussion
EM vs. MCMC
As already a 4state Gaussian HMM has 23 free parameters applying EM is often difficult due to the existence of multiple local optima and the local convergence of EM. Consequently, the estimate has to be repeated many times with randomly initialized parameter values to find the most likely model. It should also be noted that not necessarily the model maximizing the likelihood exhibits the best performance in classifying aberrations 2. Additionally, applying any constraint in an EM settings, i.e. ordering of the state means, is harder than in MCMC. EM also produces inferior results on datasets exhibiting class imbalance, for example where one type of aberrations (observations for one HMM state) are rare or missing, while MCMC can overcome this problem using informative priors. In Table 2 we see that MCMC sampling performs better than EM on real biological data which is consistent with prior reports from Guha [24] and Shah [25] who also report difficulties with EM and better MCMC performances. In particular, for HBL2 we observe that the best model in terms of F1measurewhich is not available in de novo analysisis not the most likely model and the most likely model has very low precision and, consequently, worse F1measure than MCMC sampling. On the simulated datasets, EM performs poorly if the dataset is imbalanced. As a result we observe slightly worse standard deviation for the precisions and F1measures computed by EM in Table 2. We also notice from the confusion matrix of three classesloss, neutral, and gainthat even though the mean F1measure, recall, and precision (defined on two classes, neutral and aberrant) are high, due to label switching [17], EM does not distinguish gain from loss, and vice versa, very well (see Table 3). By reordering the already learned state means the label switching problem can be addressed, but that increases misclassification rate due to state collapsing as the parameter values, specially means of the Gaussians, become almost identical [17]. In contrast, Bayesian methods cope with class imbalance problem by applying order constraints. Moreover, using McNemar's test [35] on the combined result of the 500 datasets we have verified that the predictions based on EM are significantly different from the predictions made by Bayesian methods with pvalues being less than 0.001 in both cases.
Table 3. Confusion matrices showing the proportion of accurate predictions based on EM, FBGsampling, and approximate sampling methods on 500 simulated datasets
FBG vs. Approximate Sampling
In an ideal setting, like the 2state HMM example, approximate sampling closely mimics the performance of FBG sampling up to moderate compression level. For the simulated and real dataset approximate sampling's performance is comparable to FBG's while achieving a speedup of 10 or larger. We also observe that for higher compression levels approximate sampling reports small number of aberrant clones, which results in small tp and fp values, but large fn value. As a result, we see low recall and high precision rate when the compression level is too high for a particular dataset (see the rows with w ≥ 4.0σ_{D }for HBL2 in Table 2).
From Figures 4, 5, 6, and 7 we observe that segmentations by both sampling methods are almost identical at the recommended width w value. In case of HBL2, they differ from the ground truth in some places. They predict a few extra gain regions and outliers are generally predicted as gains. We, as well as Shah et al. [25], have noticed that the HBL2 dataset has many outliers, and the variance of emission distribution of gain state 4 converges to a high value which tries to explain the outliers. In contrast, GBM has fewer outliers (see Figure 5) and approximate sampling seems robust to those outliers. As the compression algorithm forces possible outliers to be included in a compressed block, it is robust to moderate frequencies of outliers.
Width Parameter
By varying the width parameter w we can control the compression ratio γ and the speedup achieved by approximate sampling.
But from Table 1 and 2, and Lemma 1 it is also clear that by setting a large value one can get unfavorable
results. We have analyzed the effect of different w values using a synthetic dataset with a controlled level of noise following [36]. Each dataset has three chromosomes with total probe counts 500, 750, and 1000. Ten
aberrant regions of size 1120 probes, randomly assigned gain or loss, are inserted
in random positions of the 500 probe chromosome. Similarly, 15 aberrant regions of
size 1125 probes, randomly assigned gain or loss, are inserted into larger chromosomes.
A noise component N(0, σ) is added to the theoretical logratios 1,0,0.58 (loss, neutral, and gain respectively)
to model the data. For a set of noise levels, σ ranging from 0.1 to 0.5, 50 synthetic datasets are generated. We use a 3state HMM
with width parameter values in the range 0σ_{D}, ..., 4σ_{D }(where σ_{D }is the standard deviation of the dataset). Signaltonoise ratio (SNR) is defined
as
Figure 8. Effect of width parameter. F1measure (red, circle), recall (violet, square), and precision (black, triangle)
of approximate sampling over HBL2 and five synthetic datasets of varying noise levels
are shown. For comparison, F1measure (green, solid), recall (cyan, dashed dot), and
precision (olive, dotted) of FBGsampling are also shown as horizontal lines. For
width values 0.0σ_{D}, ..., 4.0σ_{D }compression ratio
Outliers
Gaussian HMMs are known to be sensitive to outliers which is evident from our results
of HBL2 and SNP arrays. Traditionally, outliers have been handled either by using
a mixture distribution as the emission distribution or by preprocessing the data to
remove possible outliers or impute more appropriate values. We have observed that
a simple local median approach works very well to identify the outliers in a time
series of log_{2}ratio values. Although using a mixture distribution or a distribution with fat tails,
i.e. Student'st distribution, is a better choice we lose a significant computational
advantage in approximate sampling. For a block of observations o' = o_{i}, ..., o_{k }we can compute
Conclusions
Analyzing CGH data either from DNA microarrays or next generation sequencing to estimate chromosomal aberrations or investigate copy number variations (CNV), leads to the problem of segmenting sequences of observations which are essentially noisy versions of piecewiseconstant functions. For reasons of efficiency, ML or MAP point estimates of HMM parameters combined with the Viterbialgorithm to compute a most likely sequence of hidden states and thus a segmentation of the input are most popular in practice. This ignores research which clearly demonstrates that Bayesian approaches, where MCMC is used for sampling and thus integrating out model parameters, is more robust with higher recall and higher precision [24]. Additionally, our experiments show that likelihood is not informative with respect to the quality of CNV calls putting the use of ML into question even if the estimation problem could be solved.
We propose a method using approximate sampling to accelerate MCMC for HMMs on such data. Our method constitutes the first use of ideas from spatial index structures for several consecutive observations and approximate computations based on geometric arguments for HMM; the effectiveness of this approach was previously demonstrated for kmeans clustering, mixture estimation, and fast evaluation of a mixture of Gaussians.
We demonstrate that these very abundant biological CGH datasets, which measure chromosomal aberrations and copy number variations, are consistent with our assumptions of piecewise constant ground truths, and we are able to achieve speedups between 10 and 60 respectively 90, on these biological datasets while maintaining competitive prediction accuracy compared to the stateoftheart. As datasets with even higher resolution, both from higher density DNA microarrays and next generation sequencing, become available, we believe that the need for precise and efficient MCMC techniques will increase. The added precision over popular ML/MAPbased methods is of particular biological relevance as for complex neurodegenerative diseases such as Autism denovo copy number variations have recently been shown to play a role [37]; a precise and quick analysis on large collectives of patients is desirable.
Applying approximate sampling to multidimensional observationsto jointly analyze data sets for recurrent CNVs [38] instead of analyzing individuals and postprocessing resultsand considering more complicated HMM topologies and observation densities are directions for our future work.
Authors' contributions
MPM and AS designed the study and wrote the manuscript. MPM implemented approximate sampling and tested it's performance. All authors read and approved the final manuscript.
Appendix
Lemma 1. Let
Proof. Using the assumption on τ, for any position i ≤ l ≤ i + n  1, we can argue that,
For any partial state path q_{i}, . . ., q_{i}_{+}_{n}_{1},
We partition S^{n}, the set of all possible partial state paths of length n, into N subsets
The size of S^{n }can be expressed in terms of total number of nonselftransitions present in a path,
As the sets
Now we define S^{[s] }= {(q_{i}, ..., q_{i}_{+}_{n}_{1}) : (q_{i }= ... = q_{i}_{+}_{n}_{1 }= s)}. For the remaining part of the proof, if Y is a set of partial state paths, we use P(Y, o'O^{i}^{1}) in place of
Now we derive an upper bound of the contribution from state paths in
Similarly, we derive an upper bound of the contribution from state paths in
Applying (5) and (6) in (4) we get,
Note: For simplicity of the notation, we follow the convention that
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