suprefine, a new supertree method

SupreFine, a new supertree method

Shel SwensonSeptember 17th 2009

Tree of Life challenges:Tree of Life challenges: - millions of species- millions of species - lots of missing data- lots of missing data

Reconstructing the Tree of Life

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Two possible approaches: - Combined Analysis - Supertree Methods

Two competing approaches

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. . . Combined Analysis

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Combined Analysis Methods

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SupertreeMethod

Two competing approaches

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. . . Combined AnalysisS

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Why use supertree methods?

• Missing data

• Large dataset sizes

• Incompatible data types (e.g., morphological features, biomolecular sequences, gene orders, even distances based upon biochemistry)

• Unavailable sequence data (only trees)

Many Supertree Methods

• MRP• weighted MRP• Min-Cut• Modified Min-Cut• Semi-strict Supertree

• MRF• MRD• QILI

• SDM• Q-imputation• PhySIC• Majority-Rule Supertrees

• Maximum Likelihood Supertrees

• and many more ...

Matrix Representation with Parsimony(Most commonly used and most accurate)

Today’s Outline

• Supertree and combined analysis methods

• Why we need better supertree methods

• SuperFine: a new supertree method that is fast and more accurate than other supertree methods– Strict Consensus Merger (SCM)

– Resolving polytomies

– Performance of SuperFine (compared to MRP and combined anaylses)

– applications and future work

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Previous Simulation Studies

2. Generate sequence

data

1. Generate Model Tree

4. ConstructSource Trees

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3. Select Subsets

5. Apply SupertreeMethod

6. Compare to Model Tree

What does lead to missing data?

• Evolution (gain and loss of genes)

• Dataset selection

• Limited resources (time, money, etc.)

My Simulation Study

1. Generate model trees (100-1000 taxa)

2. Simulate gene gain and loss and generate sequences

3. Simulate techniques for gene and taxon selection• Clade-based datasets

• Scaffold dataset

4. Generate source trees and a combined dataset

5. Apply supertree and combined analysis methods

6. Compare each estimated tree to the model tree, and record topological error

Experimental Parameters

• Number of taxa in model tree: 100, 500, and 1000– Generate 5, 15 and 25 clade-based datasets, respectively

• Scaffold density: 20%, 50%, 75%, and 100%

• Six super-methods: – Combined analysis using ML and MP– MRP on ML and MP source trees– Weighted MRP on ML and MP source trees(MRP = Matrix Representation with Parsimony)

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Quantifying Topological Error

True Tree Estimated Tree

• False positive (FP): An edge in the estimated tree not in the true tree

• False negative (FN): An edge in the true tree missing from the estimated tree

Comparison of MRP-ML and CA-ML(False Negative Rate)

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Scaffold Density (%)

We still need supertree methods!

Combined analysis cannot be used for:

– Datasets that are very large

– Incompatible data types

– Unavailable sequence data

Outline

• Supertree and combined analysis methods• Why we need better supertree methods

• SuperFine: a new supertree method that is fast and more accurate than other supertree methods– Strict Consensus Merger (SCM)

– Resolving polytomies

– Performance of SuperFine (compared to MRP and combined anaylses)

– applications and future work

Methods that Led to SuperFine

• The Strict Consensus Merger (SCM) (Huson et al. 1999)

• Quartet MaxCut (QMC)(Snir and Rao

2008)

Strict Consensus Merger (SCM)

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Theorem

Let S be a collection of source trees and T be a SCM tree on S.

Then for every s in S, ∑(T|L(s)) ∑(s), where T|L(s) is the induced subtree of T on the leafset of s.

Intuition for the Theorem

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Performance of SCM

• Low false positive (FP) rate(Estimated supertree has few false edges)

• High false negative (FN) rate(Estimated supertree is missing many true edges)

Methods that Led to SuperFine

• The Strict Consensus Merger (SCM) (Huson et al. 1999)

• Quartet MaxCut (QMC)(Snir and Rao

2008)

Quartet MaxCut (QMC)

QMC is a heuristic for the following optimization problem:

Given a collection Q of quartet trees, find a supertree T, with leaf set L(T) = qQ L(q), that displays the maximum number of quartet trees in Q.

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• 12|34, 23|45, 34|56, 45|67 are compatible quartet trees with supertree

• Adding the quartet 17|23 creates an incompatible set of quartet trees. An “optimal” supertree would be the same as above, because it agrees with 4 out of 5 quartet trees.

Maximizing # of Quartet Trees Displayed

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QMC as a Supertree Method

• Step 1: Encode source trees as a set of quartets

• Step 2: Apply QMC

Idea behind SuperFine

• First, construct a supertree with low false positives using SCM

The Strict Consensus Merger

• Then, refine the tree to reduce false negatives by resolving each polytomy using QMC Quartet Max Cut

Resolving a single polytomy, v

• Step 1: Encode each source tree as a collection of quartet trees on {1,2,...,d}, where d=degree(v)

• Step 2: Apply Quartet MaxCut (Snir and Rao) to the collection of quartet trees, to produce a tree t on leafset {1,2,...,d}

• Step 3: Replace the star tree at v by tree t

Why?

Back to Our Example

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Where We Use the Theorem

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For every s in S, ∑(T|L(s)) ∑(s)

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Step 1: Encode each source tree as a collection of

quartet trees on {1,2,...,d}

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Step 2: Apply Quartet MaxCut (QMC) to the collection of

quartet trees

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Replace polytomy using tree from QMC

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False Negative Rate

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Scaffold Density (%)

False Negative Rate

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Scaffold Density (%)

False Positive Rate

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Scaffold Density (%)

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Running Time

SuperFine vs. MRP

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MRP 8-12 sec.SuperFine 2-3 sec.

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Scaffold Density (%) Scaffold Density (%)Scaffold Density (%)

Observations

• SuperFine is much more accurate than MRP, with comparable performance only when the scaffold density is 100%

• SuperFine is almost as accurate as CA-ML

• SuperFine is extremely fast

Future Work• Exploring algorithm design space for Superfine

– Different quartet encodings

– Not using SCM in Step 1

– Parallel version

– Post-processing step to minimize Sum-of-FN to source trees

• Using Superfine to enable phylogeny estimation– without an alignment

– on many marker combined datasets

• Using Superfine in conjunction with divide-and-conquer methods to create more accurate phylogenetic methods

• Exploration of impact of source tree collections (in particular the scaffold) on supertree analyses

• Revisiting specific biological supertrees