(How to part II) Sensitively detect copy ratio alterations and allelic segments
Tutorials | Created 2018-03-26 | Last updated 2018-05-16


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Document is currently under review and in BETA. It is incomplete and may contain inaccuracies. Expect changes to the content.


This workflow is broken into two tutorials. You are currently on the second part. See Tutorial#11682 for the first part.

For this second part, at the heart is segmentation, performed by ModelSegments. In segmentation, contiguous copy ratios are grouped together into segments. The tool performs segmentation for both copy ratios and for allelic copy ratios, given allelic counts. The segmentation is informed by both types of data, i.e. the tool uses allelic data to refine copy ratio segmentation and vice versa. The tutorial refers to this multi-data approach as joint segmentation. The presented commands showcase full features of tools. It is possible to perform segmentation for each data type independently, i.e. based solely on copy ratios or based solely on allelic counts.

The tutorial illustrates the workflow using a paired sample set. Specifically, detection of allelic copy ratios uses a matched control, i.e. the HCC1143 tumor sample is analyzed using a control, the HCC1143 blood normal. It is possible to run the workflow without a matched-control. See section 8.1 for considerations in interpreting allelic copy ratio results for different modes and for different purities.

The GATK4 CNV workflow offers a multitude of levers, e.g. towards fine-tuning analyses and towards controls. Researchers are expected to tune workflow parameters on samples with similar copy number profiles as their case sample under scrutiny. Refer to each tool's documentation for descriptions of parameters.


Jump to a section

  1. Collect raw counts data with PreprocessIntervals and CollectFragmentCounts1.1 How do I view HDF5 format data?
  2. Generate a CNV panel of normals with CreateReadCountPanelOfNormals
  3. Standardize and denoise case read counts against the PoN with DenoiseReadCounts
  4. Plot standardized and denoised copy ratios with PlotDenoisedCopyRatios4.1 Compare two PoNs: considerations in panel of normals creation4.2 Compare PoN denoising versus matched-normal denoising
  5. Count ref and alt alleles at common germline variant sites using CollectAllelicCounts5.1 What is the difference between CollectAllelicCounts and GetPileupSummaries?
  6. Group contiguous copy ratios into segments with ModelSegments
  7. Call copy-neutral, amplified and deleted segments with CallCopyRatioSegments
  8. Plot modeled segments and allelic copy ratios with PlotModeledSegments8.1 Some considerations in interpreting allelic copy ratios8.2 Some results of fine-tuning smoothing parameters


5. Count ref and alt alleles at common germline variant sites using CollectAllelicCounts

CollectAllelicCounts will tabulate counts of the reference allele and counts of the dominant alternate allele for each site in a given genomic intervals list. The tutorial performs this step for both the case sample, the HCC1143 tumor, and the matched-control, the HCC1143 blood normal. This allele-specific coverage collection is just that--raw coverage collection without any statistical inferences. In the next section, ModelSegments uses the allele counts towards estimating allelic copy ratios, which in turn the tool uses to refine segmentation.

Collect allele counts for the case and the matched-control alignments independently with the same intervals. For the matched-control analysis, the allelic count sites for the case and control must match exactly. Otherwise, ModelSegments, which takes the counts in the next step, will error. Here we use an intervals list that subsets gnomAD biallelic germline SNP sites to those within the padded, preprocessed exome target intervals [9].

The tutorial has already collected allele counts for full length sample BAMs. To demonstrate coverage collection, the following command uses the small BAMs originally made for Tutorial#11136 [6]. The tutorial does not use the resulting files in subsequent steps.

Collect counts at germline variant sites for the matched-control

gatk --java-options "-Xmx3g" CollectAllelicCounts \
    -L chr17_theta_snps.interval_list \
    -I normal.bam \
    -R /gatk/ref/Homo_sapiens_assembly38.fasta \
    -O sandbox/hcc1143_N_clean.allelicCounts.tsv

Collect counts at the same sites for the case sample

gatk --java-options "-Xmx3g" CollectAllelicCounts \
    -L chr17_theta_snps.interval_list \
    -I tumor.bam \
    -R /gatk/ref/Homo_sapiens_assembly38.fasta \
    -O sandbox/hcc1143_T_clean.allelicCounts.tsv

This results in counts table files. Each data file has header lines that start with an @ asperand symbol, e.g. @HD, @SQ and @RG lines, followed by a table of data with six columns. An example snippet is shown. T_allelicCounts_header.png T_allelicCounts_snippet.png

Comments on select parameters

  • The tool requires one or more genomic intervals specified with -L. The intervals can be either a Picard-style intervals list or a VCF. See Article#1109 for descriptions of formats. The sites should represent sites of common and/or sample-specific germline variant SNPs-only sites. Omit indel-type and mixed-variant-type sites.
  • The tool requires the reference genome, specified with -R, and aligned reads, specified with -I.
  • As is the case for most GATK tools, the engine filters reads upfront using a number of read filters. Of note for CollectAllelicCounts is the MappingQualityReadFilter. By default, the tool sets the filter's --minimum-base-quality to twenty. As a result, the tool will include reads with MAPQ20 and above in the analysis [10].

☞ 5.1 What is the difference between CollectAllelicCounts and GetPileupSummaries?

Another GATK tool, GetPileupSummaries, similarly counts reference and alternate alleles. The resulting summaries are meant for use with CalculateContamination in estimating cross-sample contamination. GetPileupSummaries limits counts collections to those sites with population allele frequencies set by the parameters --minimum-population-allele-frequency and --maximum-population-allele-frequency. Details are here.

CollectAllelicCounts employs fewer engine-level read filters than GetPileupSummaries. Of note, both tools use the MappingQualityReadFilter. However, each sets a different threshold with the filter. GetPileupSummaries uses a --minimum-mapping-quality threshold of 50. In contrast, CollectAllelicCounts sets the --minimum-mapping-quality parameter to 30. In addition, CollectAllelicCounts filters on base quality. The base quality threshold is set with the --minimum-base-quality parameter, whose default is 20.

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6. Group contiguous copy ratios into segments with ModelSegments

ModelSegments groups together copy and allelic ratios that it determines are contiguous on the same segment. A Gaussian-kernel binary-segmentation algorithm differentiates ModelSegments from a GATK4.beta tool, PerformSegmentation, which GATK4 ModelSegments replaces. The older tool used a CBS (circular binary-segmentation) algorithm. ModelSegment's kernel algorithm enables efficient segmentation of dense data, e.g. that of whole genome sequences. A discussion of preliminary algorithm performance is here.

The algorithm performs segmentation for both copy ratios and for allelic copy ratios jointly when given both datatypes together. For allelic copy ratios, ModelSegments uses only those sites it determines are heterozygous, either in the control in a paired analysis or in the case in a case-only analysis [11]. In the paired analysis, the tool models allelic copy ratios in the case using sites for which the control is heterozygous. The workflow defines allelic copy ratios in terms of alternate-allele fraction, where total allele fractions for reference allele and alternate allele add to one for each site.

For the following command, be sure to specify an existing --output directory or . for the current directory.

gatk --java-options "-Xmx4g" ModelSegments \
    --denoised-copy-ratios hcc1143_T_clean.denoisedCR.tsv \
    --allelic-counts hcc1143_T_clean.allelicCounts.tsv \
    --normal-allelic-counts hcc1143_N_clean.allelicCounts.tsv \
    --output sandbox \
    --output-prefix hcc1143_T_clean

This produces nine files each with the basename hcc1143_T_clean in the current directory and listed below. The param files contain global parameters for copy ratios (cr) and allele fractions (af), and the seg files contain data on the segments. For either type of data, the tool gives data before and after segmentation smoothing. The tool documentation details what each file contains. The last two files, labeled hets, contain the allelic counts for the control's heterogygous sites. Counts are for the matched control (normal) and the case.

  1. hcc1143_T_clean.modelBegin.seg
  2. hcc1143_T_clean.modelFinal.seg
  3. hcc1143_T_clean.cr.seg
  4. hcc1143_T_clean.modelBegin.af.param
  5. hcc1143_T_clean.modelBegin.cr.param
  6. hcc1143_T_clean.modelFinal.af.param
  7. hcc1143_T_clean.modelFinal.cr.param
  8. hcc1143_T_clean.hets.normal.tsv
  9. hcc1143_T_clean.hets.tsv

The tool has numerous adjustable parameters and these are described in the ModelSegments tool documentation. The tutorial uses the default values for all of the parameters. Adjusting parameters can change the resolution and smoothness of the segmentation results.

Comments on select parameters

  • The tool accepts both or either copy-ratios (--denoised-copy-ratios) or allelic-counts (--allelic-counts) data. The matched-control allelic counts (--normal-allelic-counts) is optional. If given both types of data, then copy ratios and allelic counts data together inform segmentation for both copy ratio and allelic segments. If given only one type of data, then segmentation is based solely on the given type of data.
  • The --minimum-total-allele-count is set to 30 by default. This means the tool only considers sites with 30 or more read depth coverage for allelic copy ratios.
  • The --genotyping-homozygous-log-ratio-threshold option is set to -10.0 by default. Increase this to increase the number of sites assumed to be heterozygous for modeling.
  • Default smoothing parameters are optimized for faster performance, given the size of whole genomes. The --maximum-number-of-smoothing-iterations option caps smoothing iterations to 25. MCMC model sampling is also set to 100, for both copy-ratio and allele-fraction sampling by the --number-of-samples-copy-ratio and --number-of-samples-allele-fraction options, respectively. Finally, --number-of-smoothing-iterations-per-fit is set to zero by default to disable model refitting between iterations. What this means is that the tool will generate only two MCMC fits--an initial and a final fit.
    • GATK4.beta's ACNV set this parameter such that each smoothing iteration refit using MCMC, at the cost of compute. For the tutorial data, which is targeted exomes, the default zero gives 398 segments after two smoothing iterations, while setting --number-of-smoothing-iterations-per-fit to one gives 311 segments after seven smoothing iterations. Section 8 plots these alternative results.
  • For advanced smoothing recommendations, see [12].

Section 8 shows the results of segmentation, the result from changing --number-of-smoothing-iterations-per-fit and the result of allelic segmentation modeled from allelic counts data alone. Section 8.1 details considerations depending on analysis approach and purity of samples. Section 8.2 shows the results of changing the advanced smoothing parameters given in [12].

ModelSegments runs in the following three stages.

  1. Genotypes heterozygous sites and filters on depth and for sites that overlap with copy-ratio intervals.
    • Allelic counts for sites in the control that are heterozygous are written to hets.normal.tsv. For the same sites in the case, allelic counts are written to hets.tsv.
    • If given only allelic counts data, ModelSegments does not apply intervals.
  2. Performs multidimensional kernel segmentation (1, 2).
    • Uses allelic counts within each copy-ratio interval for each contig.
    • Uses denoised copy ratios and heterozygous allelic counts.
  3. Performs Markov-Chain Monte Carlo (MCMC, 1, 2, 3) sampling and segment smoothing. In particular, the tool uses Gibbs sampling and slice sampling. These MCMC samplings inform smoothing, i.e. merging adjacent segments, and the tool can perform multiple iterations of sampling and smoothing [13].
    • Fits initial model. Writes initial segments to modelBegin.seg, posterior summaries for copy-ratio global parameters to modelBegin.cr.param and allele-fraction global parameters to modelBegin.af.param.
    • Iteratively performs segment smoothing and sampling. Fits allele-fraction model [14] until log likelihood converges. This process produces global parameters.
    • Samples final models. Writes final segments to modelFinal.seg, posterior summaries for copy-ratio global parameters to modelFinal.cr.param, posterior summaries for allele-fraction global parameters to modelFinal.af.param and final copy-ratio segments to cr.seg.

At the second stage, the tutorial data generates the following message.

INFO  MultidimensionalKernelSegmenter - Found 638 segments in 23 chromosomes.

At the third stage, the tutorial data generates the following message.

INFO  MultidimensionalModeller - Final number of segments after smoothing: 398

For tutorial data, the initial number of segments before smoothing is 638 segments over 23 contigs. After smoothing with default parameters, the number of segments is 398 segments.

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7. Call copy-neutral, amplified and deleted segments with CallCopyRatioSegments

CallCopyRatioSegments allows for systematic calling of copy-neutral, amplified and deleted segments. This step is not required for plotting segmentation results. Provide the tool with the cr.seg segmentation result from ModelSegments.

gatk CallCopyRatioSegments \
    --input hcc1143_T_clean.cr.seg \
    --output sandbox/hcc1143_T_clean.called.seg

The resulting called.seg data adds the sixth column to the provided copy ratio segmentation table. The tool denotes amplifications with a + plus sign, deletions with a - minus sign and neutral segments with a 0 zero.

Here is a snippet of the results. T_called_seg.png

Comments on select parameters

  • The parameters --neutral-segment-copy-ratio-lower-bound (default 0.9) and --neutral-segment-copy-ratio-upper-bound (default 1.1) together set the copy ratio range for copy-neutral segments. These two parameters replace the GATK4.beta workflow’s --neutral-segment-copy-ratio-threshold option.

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8. Plot modeled copy ratio and allelic fraction segments with PlotModeledSegments

PlotModeledSegments visualizes copy and allelic ratio segmentation results.

gatk PlotModeledSegments \
    --denoised-copy-ratios hcc1143_T_clean.denoisedCR.tsv \
    --allelic-counts hcc1143_T_clean.hets.tsv \
    --segments hcc1143_T_clean.modelFinal.seg \
    --sequence-dictionary Homo_sapiens_assembly38.dict \
    --minimum-contig-length 46709983 \
    --output sandbox/plots \
    --output-prefix hcc1143_T_clean

This produces plots in the plots folder. The plots represent final modeled segments for both copy ratios and alternate allele fractions. If we are curious about the extent of smoothing provided by MCMC, then we can similarly plot initial kernel segmentation results by substituting in --segments hcc1143_T_clean.modelBegin.seg.

Comments on select parameters

  • The tutorial provides the --sequence-dictionary that matches the GRCh38 reference used in mapping [4].
  • To omit alternate and decoy contigs from the plots, the tutorial adjusts the --minimum-contig-length from the default value of 1,000,000 to 46,709,983, the length of the smallest of GRCh38's primary assembly contigs.

As of this writing, it is NOT possible to subset plotting with genomic intervals, i.e. with the -L parameter. To interactively visualize data, consider the following options.

  • Modify the sequence dictionary to contain only the contigs of interest, in the order desired.
  • The bedGraph format for targeted exomes and bigWig for whole genomes. An example of CNV data converted to bedGraph and visualized in IGV is given in this discussion.
  • Alternatively, researchers versed in R may choose to visualize subsets of data using RStudio.

Below are three sets of results for the HCC1143 tumor cell line in order of increasing smoothing. The top plot of each set shows the copy ratio segments. The bottom plot of each set shows the allele fraction segments.

  • In the denoised copy ratio segment plot, individual targets still display as points on the plot. Different copy ratio segments are indicated by alternating blue and orange color groups. The denoised median is drawn in thick black.
  • In the allele fraction plot, the boxes surrounding the alternate allelic fractions do NOT indicate standard deviation nor standard error, which biomedical researchers may be more familiar with. Rather, the allelic fraction data is given in credible intervals. The allelic copy ratio plot shows the 10th, 50th and 90th percentiles. These should be interpreted with care as explained in section 8.1. Individual allele fraction data display as faint data points, also in orange and blue.

8A. Initial segmentation before MCMC smoothing gives 638 segments. T_modelbegin.modeled.png

8B. Default smoothing gives 398 segments. T_modelfinal.modeled.png

8C. Enabling additional smoothing iterations per fit gives 311 segments. See section 6 for a description of the --number-of-smoothing-iterations-per-fit parameter. T_increase_smoothing_1.modeled.png

Smoothing accounts for data points that are outliers. Some of these outliers could be artifactual and therefore not of interest, while others could be true copy number variation that would then be missed. To understand the impact of joint copy ratio and allelic counts segmentation, compare the results of 8B to the single-data segmentation results below. Each plot below shows the results of modeling segmentation on a single type of data, either copy-ratios or allelic counts, using default smoothing parameters.

8D. Copy ratio segmentation based on copy ratios alone gives 235 segments. T_caseonly.modeled.png

8E. Allelic segmentation result based on allelic counts alone in the matched case gives 105 segments. T-matched-normal_just_allelic.modeled.png

Compare chr1 and chr2 segmentation for the various plots. In particular, pay attention to the p arm (left side) of chr1 and q arm (right side) of chr2. What do you think is happening when adjacent segments are slightly shifted from each other in some sets but then seemingly at the same copy ratio for other sets?

For allelic counts, ModelSegments retains 16,872 sites that are heterozygous in the control. Of these, the case presents 15,486 usable sites. In allelic segmentation using allelic counts alone, the tool uses all of the usable sites. In the matched-control scenario, ModelSegments emits the following message.

INFO  MultidimensionalKernelSegmenter - Using first allelic-count site in each copy-ratio interval (12668 / 15486) for multidimensional segmentation...

The message informs us that for the matched-control scenario, ModelSegments uses the first allele-count site for each genomic interval towards allelic modeling. For the tutorial data, this is 12,668 out of the 15,486 or 81.8% of the usable allele-count sites. The exclusion of ~20% of allelic-counts sites, together with the lack of copy ratio data informing segmentation, account for the difference we observe in this and the previous allelic segmentation plot.

In the allele fraction plot, some of the alternate-allele fractions are around 0.35/0.65 and some are at 0/1. We also see alternate-allele fractions around 0.25/0.75 and 0.5. These suggest ploidies that are multiples of one, two, three and four.

Is it possible a copy ratio of one is not diploid but represents some other ploidy?

For the plots above, focus on chr4, chr5 and chr17. Based on both the copy ratio and allelic results, what is the zygosity of each of the chromosomes? What proportion of each chromosome could be described as having undergone copy-neutral loss of heterozygosity?


☞ 8.1 Some considerations in interpreting allelic copy ratios

For allelic copy ratio analysis, the matched-control is a sample from the same individual as the case sample. In the somatic case, the matched-control is the germline normal sample and the case is the tumor sample from the same individual.

The matched-control case presents the following considerations.

  • If a matched control contains any region with copy number amplification, the skewed allele fractions still allow correct interpretation of the original heterozygosity.
  • However, if a matched control contains deleted regions or regions with copy-neutral loss of heterozygosity or a long stretch of homozygosity, e.g. as occurs in uniparental disomy, then these regions would go dark so to speak in that they become apparently homozygous and so ModelSegments drops them from consideration.
  • From population sequencing projects, we know the expected heterozygosity of normal germline samples averages around one in a thousand. However, the GATK4 CNV workflow does not account for any heterozygosity expectations. An example of such an analysis that utilizes SNP array data is HAPSEG. It is available on GenePattern.
  • If a matched normal contains tumor contamination, this should still allow for the normal to serve as a control. The expectation is that somatic mutations coinciding with common germline SNP sites will be rare and ModelSegments (i) only counts the dominant alt allele at multiallelic sites and (ii) recognizes and handles outliers. To estimate tumor in normal (TiN) contamination, see the Broad CGA group's deTiN.

Here are some considerations for detecting loss of heterozygosity regions.

  • In the matched-control case, if the case sample is pure, i.e. not contaminated with the control sample, then we see loss of heterozygosity (LOH) segments near alternate-allele fractions of zero and one.
  • If the case is contaminated with matched control, whether the analysis is matched or not, then the range of alternate-allele fractions becomes squished so to speak in that the contaminating normal's heterozygous sites add to the allele fractions. In this case, putative LOH segments still appear at the top and bottom edges of the allelic plot, at the lowest and highest alternate-allele fractions. For a given depth of coverage, the fraction of reads that differentiates zygosity is narrower in range and therefore harder to differentiate visually.

    8F. Case-only analysis of tumor contaminated with normal still allows for LOH detection. Here, we bluntly added together the tutorial tumor and normal sample reads. Results for the matched-control analysis are similar. mixTN_tumoronly.modeled.png

  • In the tumor-only case, if the tumor is pure, because ModelSegments drops homozygous sites from consideration and only models sites it determines are heterozygous, the workflow cannot ascertain LOH segments. Such LOH regions may present as an absence of allelic data or as low confidence segments, i.e. having a wide confidence interval on the allelic plot. Compare such a result below to that of the matched case in 8E above.

    8G. Allelic segmentation result based on allelic counts alone for case-only, when the case is pure, can produce regions of missing representation and low confidence allelic fraction segments. T-only_just_allelic.modeled.png

    Compare results. Focus on chr4, chr5 and chr17. While the matched-case gives homozygous zygosity for each of these chromosomes, the case-only allelic segmentation either presents an absence of segments for regions or gives low confidence allelic fraction segments at alternate allele fractions that are inaccurate, i.e. do not represent actual zygosity. This is particularly true for tumor samples where aneuploidy and LOH are common. Interpret case-only allelic results with caution.

Finally, remember the tutorial analyses above utilize allelic counts from gnomAD sites of common population variation that have been lifted-over from GRCh37 to GRCh38. For allelic count sites, use of sample-specific germline variant sites may incrementally increase resolution. Also, use of confident variant sites from a callset derived from alignments to the target reference may help decrease noise. Confident germline variant sites can be derived with HaplotypeCaller calling on alignments and subsequent variant filtration. Alternatively, it is possible to fine-tune ModelSegments smoothing parameters to dampen noise.


☞ 8.2 Some results of fine-tuning smoothing parameters

This section shows plotting results of changing some advanced smoothing parameters. The parameters and their defaults are given below, in the order of recommended consideration [12].

--number-of-changepoints-penalty-factor 1.0 \
--kernel-variance-allele-fraction 0.025 \
--kernel-variance-copy-ratio 0.0 \
--kernel-scaling-allele-fraction 1.0 \
--smoothing-credible-interval-threshold-allele-fraction 2.0 \
--smoothing-credible-interval-threshold-copy-ratio 2.0 \

The first four parameters impact segmentation while the last two parameters impact modeling. The following plots show the results of changing these smoothing parameters. The tutorial chose argument values arbitrarily, for illustration purposes. Results should be compared to that of 8B, which gives 398 segments.

8H. Increasing changepoints penalty factor from 1.0 to 5.0 gives 140 segments.

8I. Increasing kernel variance parameters each to 0.8 gives 144 segments. Changing --kernel-variance-copy-ratio alone to 0.025 increases the number of segments greatly, to 1,266 segments. Changing it to 0.2 gives 414 segments.

8J. Decreasing kernel scaling from 1.0 to 0 gives 236 segments. Conversely, increasing kernel scaling from 1.0 to 5.0 gives 551 segments.

8K. Increasing both smoothing parameters each from 2.0 to 10.0 gives 263 segments.

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Footnotes

[9] The GATK Resource Bundle provides two variations of a SNPs-only gnomAD project resource VCF. Both VCFs are sites-only eight-column VCFs but one retains the AC allele count and AF allele frequency variant-allele-specific annotations, while the other removes these to reduce file size.

  • For targeted exomes, it may be convenient to subset these to the preprocessed intervals, e.g. with SelectVariants for use with CollectAllelicCounts. This is not necessary, however, as ModelSegments drops sites outside the target regions from its analysis in the joint-analysis approach.
  • For whole genomes, depending on the desired resolution of the analysis, consider subsetting the gnomAD sites to those commonly variant, e.g. above an allele frequency threshold. Note that SelectVariants, as of this writing, can filter on AF allele frequency only for biallelic sites. Non-biallelic sites make up ~3% of the gnomAD SNPs-only resource.
  • For more resolution, consider adding sample-specific germline variant biallelic SNPs-only sites to the intervals. Section 8.1 shows allelic segmentation results for such an analysis.

[10] The MAPQ20 threshold of CollectAllelicCounts is lower than that used by CollectFragmentCounts, which uses MAPQ30.

[11] In particular, the tool considers only heterozygous sites that have counts for both the reference allele and the alternate allele. If multiple alternate alleles are present, the tool uses the alternate allele with the highest count and ignores any other alternate allele(s).

[12] These advanced smoothing recommendations are from one of the workflow developers--@slee.

  • For smoother results, first increase --number-of-changepoints-penalty-factor from its default of 1.0.
  • If the above does not suffice, then consider changing the kernel-variance parameters --kernel-variance-copy-ratio (default 0.0) and --kernel-variance-allele-fraction (default 0.025), or change the weighting of the allele-fraction data by changing --kernel-scaling-allele-fraction (default 1.0).
  • If such changes are still insufficient, then consider adjusting the smoothing-credible-interval-threshold parameters --smoothing-credible-interval-threshold-copy-ratio (default 2.0) and --smoothing-credible-interval-threshold-allele-fraction (default 2.0). Increasing these will more aggressively merge adjacent segments.

[13] In particular, uses Gibbs sampling, a type of MCMC sampling, towards both allele-fraction modeling and copy-ratio modeling, and additionally uses slice sampling towards allele-fraction modeling. @slee details the following substeps.

  1. Perform MCMC (Gibbs) to fit the copy-ratio model posteriors.
  2. Use optimization (of the log likelihood) to initialize the Markov Chain for the allele-fraction model.
  3. Perform MCMC (Gibbs and slice) to fit the allele-fraction model posteriors.
  4. The initial model is now fit. We write the corresponding modelBegin files, including those for global parameters.
  5. Iteratively perform segment smoothing.
  6. Perform steps 1-4 again, this time to generate the final model fit and modelFinal files.

[14] @slee shares the tool initializes the MCMC by starting off at the maximum a posteriori (MAP) point in parameter space.

Many thanks to Samuel Lee (@slee), no relation, for his patient explanations of the workflow parameters and mathematics behind the workflow. Researchers may find reading through @slee's comments on the forum, a few of which the tutorials link to, insightful.

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