Applications of Analytic Combinatorics in Mathematical Biology (joint with H. Chang, M. Drmota, E. Y. Jin, and Y.-W. Lee)
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1 Applications of Analytic Combinatorics in Mathematical Biology (joint with H. Chang, M. Drmota, E. Y. Jin, and Y.-W. Lee) Michael Fuchs Department of Applied Mathematics National Chiao Tung University June 30th, 2015 Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
2 Analytic Combinatorics (i) Combinatorialists use recurrence, generating functions, and such transformations as the Vandermonde convolution; others, to my horror, use contour integrals, differential equations, and other resources of mathematical analysis. - John Riordan (1968). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
3 Analytic Combinatorics (ii) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
4 Outline of the Talk 1 Introduction Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
5 Outline of the Talk 1 Introduction 2 Patterns in Phylogenetic Trees H. Chang and M. Fuchs (2010). Limit theorems for patterns in phylogenetic trees, J. Math. Biol., 60:4, Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
6 Outline of the Talk 1 Introduction 2 Patterns in Phylogenetic Trees H. Chang and M. Fuchs (2010). Limit theorems for patterns in phylogenetic trees, J. Math. Biol., 60:4, Shapley Value and Fair Proportion Index M. Fuchs and E. Y. Jin (2015). Equality of Shapley value and fair proportion index in phylogenetic trees, J. Math. Biol., in press. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
7 Outline of the Talk 1 Introduction 2 Patterns in Phylogenetic Trees H. Chang and M. Fuchs (2010). Limit theorems for patterns in phylogenetic trees, J. Math. Biol., 60:4, Shapley Value and Fair Proportion Index M. Fuchs and E. Y. Jin (2015). Equality of Shapley value and fair proportion index in phylogenetic trees, J. Math. Biol., in press. 4 Number of Groups formed by Social Animals M. Drmota, M. Fuchs, Y.-W. Lee (2016+). Stochastic analysis of the extra clustering model for animal grouping, in revision. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
8 Evolutionary Biology Charles Darwin ( ) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
9 Evolutionary Biology First notebook on Transmutation of Species (1837) Charles Darwin ( ) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
10 Phylogenetic trees (=PTs) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
11 Phylogenetic trees (=PTs) Phylogenetic tree of size n: rooted, plane, unlabelled binary tree with n external nodes (and consequently n 1 internal nodes). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
12 Applications of PTs Understanding genetic relatedness of species Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
13 Applications of PTs Understanding genetic relatedness of species Understanding the underlying evolutionary process Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
14 Applications of PTs Understanding genetic relatedness of species Understanding the underlying evolutionary process Predicting possible future outcomes Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
15 Applications of PTs Understanding genetic relatedness of species Understanding the underlying evolutionary process Predicting possible future outcomes Testing appropriateness of random models Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
16 Applications of PTs Understanding genetic relatedness of species Understanding the underlying evolutionary process Predicting possible future outcomes Testing appropriateness of random models Making conservation decisions in genetics Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
17 Applications of PTs Understanding genetic relatedness of species Understanding the underlying evolutionary process Predicting possible future outcomes Testing appropriateness of random models Making conservation decisions in genetics Modeling the group formation process of social animals Etc. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
18 Yule-Harding model or Kingman Coalescent Example: time species Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
19 Yule-Harding model or Kingman Coalescent Example: Random Model 1: At every time point, two yellow nodes uniformly coalescent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
20 Yule-Harding model or Kingman Coalescent Example: Random Model 1: At every time point, two yellow nodes uniformly coalescent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
21 Yule-Harding model or Kingman Coalescent Example: Random Model 1: At every time point, two yellow nodes uniformly coalescent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
22 Yule-Harding model or Kingman Coalescent Example: Random Model 1: At every time point, two yellow nodes uniformly coalescent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
23 Yule-Harding model or Kingman Coalescent Example: Random Model 1: At every time point, two yellow nodes uniformly coalescent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
24 Yule-Harding model or Kingman Coalescent Example: Random Model 1: At every time point, two yellow nodes uniformly coalescent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
25 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
26 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
27 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
28 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
29 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
30 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
31 Yule-Harding model or Increasing Binary Trees Example: Random Model 2: At every time point, a yellow node is replaced by a cherry. Random model 1 and random model 2 are the same. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
32 Binary Search Trees Example: Input 1, 4, 2, 5, 3 Random Model 3: All permutations of the input are equally equally. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
33 Binary Search Trees Example: Input 1, 4, 2, 5, 3 1 Random Model 3: All permutations of the input are equally equally. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
34 Binary Search Trees Example: Input 1, 4, 2, 5, Random Model 3: All permutations of the input are equally equally. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
35 Binary Search Trees Example: Input 1, 4, 2, 5, Random Model 3: All permutations of the input are equally equally. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
36 Binary Search Trees Example: Input 1, 4, 2, 5, 3 1 Random Model 3: All permutations of the input are equally equally. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
37 Binary Search Trees Example: Input 1, 4, 2, 5, 3 1 Random Model 3: All permutations of the input are equally equally. 3 Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
38 Binary Search Trees Example: Input 1, 4, 2, 5, 3 1 Random Model 3: All permutations of the input are equally equally. 3 Again the same as random model 1 and random model 2. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
39 Patterns in PTs k-pronged node (McKenzie and Steel 2000; Rosenberg 2006): Node with an induced subtree of size k. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
40 Patterns in PTs k-pronged node (McKenzie and Steel 2000; Rosenberg 2006): Node with an induced subtree of size k. k-caterpillar (Rosenberg 2006): Induced subtree of size k with an internal node which is descendent of all other internal nodes. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
41 Patterns in PTs k-pronged node (McKenzie and Steel 2000; Rosenberg 2006): Node with an induced subtree of size k. k-caterpillar (Rosenberg 2006): Induced subtree of size k with an internal node which is descendent of all other internal nodes. Node with minimal clade size k 3 (Blum and François (2005)): Node with induced subtree of size k and either right or left subtree is an external node. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
42 Mean and variance of k-pronged nodes X n,k = # of k-pronged nodes in random PT of size n. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
43 Mean and variance of k-pronged nodes X n,k = # of k-pronged nodes in random PT of size n. Rosenberg 2006: we have µ n,k := E(X n,k ) = 2n, (n > k) k(k + 1) and 2(4k 2 3k 4)(k 1)n k(k + 1) 2, if n > 2k; (2k 1)(2k + 1) σn,k 2 := Var(X 2(5k 7)(k 1) n,k) = (k + 1) 2, if n = 2k; (2k 1) 2(k 2 + k 2n)n k 2 (k + 1) 2, if 2k > n > k. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
44 Mean and variance of k-pronged nodes X n,k = # of k-pronged nodes in random PT of size n. Rosenberg 2006: we have µ n,k := E(X n,k ) = 2n, (n > k) k(k + 1) and 2(4k 2 3k 4)(k 1)n k(k + 1) 2, if n > 2k; (2k 1)(2k + 1) σn,k 2 := Var(X 2(5k 7)(k 1) n,k) = (k + 1) 2, if n = 2k; (2k 1) 2(k 2 + k 2n)n k 2 (k + 1) 2, if 2k > n > k. This result + central limit theorem also obtained by Devroye in 1991! Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
45 The phase change Question: What happens for k as n? Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
46 The phase change Question: What happens for k as n? Theorem (Feng, Mahmoud, Panholzer; 2008) (i) (Normal range) Let k = o ( n). Then, X n,k µ n,k σ n,k (ii) (Poisson range) Let k c n. Then, X n,k d N (0, 1). d Po(2c 2 ). (iii) (Degenerate range) Let k < n and n = o(k). Then, L X 1 n,k 0. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
47 A General Framework A pattern of size k is a set of induced subtrees of size k. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
48 A General Framework A pattern of size k is a set of induced subtrees of size k. X n,k = # of occurrence of a pattern of size k in random PT of size n. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
49 A General Framework A pattern of size k is a set of induced subtrees of size k. X n,k = # of occurrence of a pattern of size k in random PT of size n. We have, d X n,k = XIn,k + Xn I, n,k where X k,k = Bernoulli(p k ), X In,k and Xn I n,k are conditionally independent given I n, and I n = Unif{1,..., n 1} Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
50 A General Framework A pattern of size k is a set of induced subtrees of size k. X n,k = # of occurrence of a pattern of size k in random PT of size n. We have, d X n,k = XIn,k + Xn I, n,k where X k,k = Bernoulli(p k ), X In,k and Xn I n,k are conditionally independent given I n, and I n = Unif{1,..., n 1} Here, p k shape parameter 1 # of k-pronged nodes 2/(k 1) # of nodes with minimal clade size k 2 k 2 /(k 1)! # of k caterpillars Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
51 Mean Value and Variance We have and for n > 2k. µ n,k = 2p kn k(k + 1), (n > k), σ 2 n,k = 2(4k3 + 4k 2 k 1 (11k 2 5)p k )p k n k(k + 1) 2 (2k 1)(2k + 1) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
52 Mean Value and Variance We have and for n > 2k. µ n,k = 2p kn k(k + 1), (n > k), σ 2 n,k = 2(4k3 + 4k 2 k 1 (11k 2 5)p k )p k n k(k + 1) 2 (2k 1)(2k + 1) Note that for n > 2k and k. µ n,k σ 2 n,k 2p kn k 2 Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
53 Poisson Approximation Theorem (Chang and F.; 2010) Let k < n and k. Then, d T V (X n,k, Po(µ n,k )) = { O (p k /k), if µ n,k 1; O (p k /k µ n,k ), if µ n,k < 1. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
54 Poisson Approximation Theorem (Chang and F.; 2010) Let k < n and k. Then, d T V (X n,k, Po(µ n,k )) = { O (p k /k), if µ n,k 1; O (p k /k µ n,k ), if µ n,k < 1. Recently re-proved by Holmgren and Janson with Stein s method: C. Holmgren and S. Janson (2015). Limit laws for functions of fringe trees for binary search trees and recursive trees, Electronic J. Probability, 20:4, Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
55 Berry-Esseen bound and LLT Theorem (Chang and F.; 2010) For µ n,k, ( sup P X n,k µ n,k σ n,k x R ) < x Φ(x) = O ( ) k. pk n Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
56 Berry-Esseen bound and LLT Theorem (Chang and F.; 2010) For µ n,k, ( sup P X n,k µ n,k σ n,k x R ) < x Φ(x) = O ( ) k. pk n Theorem (Chang and F.; 2010) For µ n,k, ( P (X n,k = µ n,k + xσn,k 2 ) = e x2 /2 (1 + O (1 + x 3 ) 2πσn,k k pk n )), uniformly in x = o((p k n) 1/6 /k 1/3 ). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
57 Short Summary We have proved: Explicit expressions for mean and variance. Local limit theorem + Berry-Esseen bound for the largest possible range of k. Poisson approximation + rate whenever k. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
58 Short Summary We have proved: Explicit expressions for mean and variance. Local limit theorem + Berry-Esseen bound for the largest possible range of k. Poisson approximation + rate whenever k. So, for k n the number of occurrences of the pattern can be approximated by the normal distribution, whereas for the remaining range a Poisson random variable should be used. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
59 Short Summary We have proved: Explicit expressions for mean and variance. Local limit theorem + Berry-Esseen bound for the largest possible range of k. Poisson approximation + rate whenever k. So, for k n the number of occurrences of the pattern can be approximated by the normal distribution, whereas for the remaining range a Poisson random variable should be used. In particular, the phase change occurs much earlier than predicted by Feng, Mahmoud, and Panholzer. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
60 Lloyd Shapley Lloyd Shapley (1923-) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
61 Lloyd Shapley Shapley value: Measure of importance of each player in a cooperative game Lloyd Shapley (1923-) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
62 Lloyd Shapley Shapley value: Measure of importance of each player in a cooperative game recently used as prioritization tool of taxa in phylogenetics Lloyd Shapley (1923-) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
63 Shapley Value and Modified Shapley Value T... PT; a... taxon (=leaf) of T. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
64 Shapley Value and Modified Shapley Value T... PT; a... taxon (=leaf) of T. Shapley value SV T (a): SV T (a) = 1 ( S 1)!(n S )!(PD T (S) PD T (S \ {a})). n! S,a S Modified Shapley value SV T (a): SV T (a) = 1 ( S 1)!(n S )!(PD T (S) PD T (S \ {a})). n! S 2,a S PD(S) is the size of the ancestor of S. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
65 Fair Proportion Index Fair proportion index FP T (a): FP T (a) = e 1 D e with D e the number of taxa below e. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
66 Fair Proportion Index Fair proportion index FP T (a): FP T (a) = e 1 D e with D e the number of taxa below e. Selected (somehow arbitrarily) by Zoological Society of London for EDGE of Existence conservation program! Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
67 Fair Proportion Index Fair proportion index FP T (a): FP T (a) = e 1 D e with D e the number of taxa below e. Selected (somehow arbitrarily) by Zoological Society of London for EDGE of Existence conservation program! FP n = fair proportion index of random taxon in random PT of size n: { 1 j FP n (I n = j) = + FP j, with probability j/n; 1 n j + FP n j, with probability (n j)/n, where I n = Unif{1,..., n 1}. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
68 Strong Correlation between SV and FP Hartmann (2013): Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
69 SV = FP Assume a is in left subtree T l and T l = j. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
70 SV = FP Assume a is in left subtree T l and T l = j. Lemma We have, and SV T (a) = 1 j + SV T l (a) FP T (a) = 1 j + FP T l (a). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
71 SV = FP Assume a is in left subtree T l and T l = j. Lemma We have, and SV T (a) = 1 j + SV T l (a) FP T (a) = 1 j + FP T l (a). Theorem (F. and Jin; 2015) We have, SV T (a) = FP T (a). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
72 SV and FP (i) D T (a) = depth of a in T : FP T (a) = SV T (a) = SV T (a) + D T (a) n. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
73 SV and FP (i) D T (a) = depth of a in T : FP T (a) = SV T (a) = SV T (a) + D T (a) n. D n = depth of random taxon in random PT of size n. Lemma We have, Var(FP n ) = 10 6H (2) n 1 6 n 4 10 π2 n2 Var(D n ) = 2H n 4H n (2) log n, where H n = 1 j n 1/j and H(2) n = 1 j n 1/j2. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
74 SV and FP (ii) Lemma We have, Cov(FP n, D n ) = 4H (2) n n + 4 n 2 2π Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
75 SV and FP (ii) Lemma We have, Cov(FP n, D n ) = 4H (2) n n + 4 n 2 2π Theorem (F. and Jin; 2015) The correlation coefficient ρ( SV n, FP n ) of modified Shapley value and fair proportion index tends to 1, i.e., lim ρ( SV n, FP n ) = 1. n Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
76 Short Summary Shapley value was introduced for the purpose of making conservation decisions in genetics. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
77 Short Summary Shapley value was introduced for the purpose of making conservation decisions in genetics. Fair proportion index was used by Zoological Society of London but its biodiversity value was unclear. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
78 Short Summary Shapley value was introduced for the purpose of making conservation decisions in genetics. Fair proportion index was used by Zoological Society of London but its biodiversity value was unclear. Strong correlation between modified Shapley value and fair proportion index was observed by Hartmann and others. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
79 Short Summary Shapley value was introduced for the purpose of making conservation decisions in genetics. Fair proportion index was used by Zoological Society of London but its biodiversity value was unclear. Strong correlation between modified Shapley value and fair proportion index was observed by Hartmann and others. We proved that Shapley value equals fair proportion index. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
80 Short Summary Shapley value was introduced for the purpose of making conservation decisions in genetics. Fair proportion index was used by Zoological Society of London but its biodiversity value was unclear. Strong correlation between modified Shapley value and fair proportion index was observed by Hartmann and others. We proved that Shapley value equals fair proportion index. We showed that correlation coefficient of modified Shapley value and fair proportion index tends to 1 in Yule-Harding model and other random models. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
81 Social Animals Consider n animals of a class of social animals. Construct a random PT with leaves representing the animals. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
82 Social Animals Consider n animals of a class of social animals. Construct a random PT with leaves representing the animals. Describes the genetic relatedness of the animals. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
83 Animal Groups Durand, Blum and François (2007): Groups are formed more likely by animals which are genetically related. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
84 Animal Groups Durand, Blum and François (2007): Groups are formed more likely by animals which are genetically related. neutral model. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
85 Animal Groups Durand, Blum and François (2007): Groups are formed more likely by animals which are genetically related. neutral model. Clade of a leaf: All leafs of the tree rooted at the parent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
86 Animal Groups Durand, Blum and François (2007): Groups are formed more likely by animals which are genetically related. neutral model. Clade of a leaf: All leafs of the tree rooted at the parent. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
87 Animal Groups Durand, Blum and François (2007): Groups are formed more likely by animals which are genetically related. neutral model. # of groups # of maximal clades Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
88 Animal Groups Durand, Blum and François (2007): Groups are formed more likely by animals which are genetically related. neutral model. # of groups # of maximal clades 2 Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
89 # of Groups X n = # of groups under the Yule Harding model Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
90 # of Groups X n = # of groups under the Yule Harding model We have, { d 1, if I n = 1 or I n = n 1, X n = X In + Xn I n, otherwise, where I n = Uniform{1,..., n 1} is the # of animals in the left subtree and X n is an independent copy of X n. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
91 # of Groups X n = # of groups under the Yule Harding model We have, { d 1, if I n = 1 or I n = n 1, X n = X In + Xn I n, otherwise, where I n = Uniform{1,..., n 1} is the # of animals in the left subtree and X n is an independent copy of X n. Theorem (Durand and François; 2010) We have, E(X n ) an (a := 1 e 2 4 ). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
92 Comparison with Real-life Data Durand, Blum and François (2007) presented the following data: Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
93 Yi-Wen s Thesis (2012) Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
94 Variance and SLLN Theorem (Lee; 2012) We have, Var(X n ) (1 e 2 ) 2 n log n = 4a 2 n log n. 4 Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
95 Variance and SLLN Theorem (Lee; 2012) We have, Var(X n ) (1 e 2 ) 2 n log n = 4a 2 n log n. 4 Theorem (Lee; 2012) We have, ( ) P lim X n n E(X n ) 1 = 0 = 1. For SLLN, X n is constructed on the same probability space via the tree evolution process underlying the Yule-Harding model. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
96 Method of Moments Theorem Assume that E(X k n) E(X k ) for all k 1 and that X is uniquely characterised by its sequence of moments. Then, X n d X. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
97 Method of Moments Theorem Assume that E(X k n) E(X k ) for all k 1 and that X is uniquely characterised by its sequence of moments. Then, X n d X. Many sufficient conditions for X being uniquely characterized by its moments are known, e.g., E(X k ) zk k! k 1 has a positive radius of convergence. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
98 Higher Moments Theorem (Lee; 2012) For all k 3, E(X n E(X n )) k ( 1) k 2k k 2 ak n k 1. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
99 Higher Moments Theorem (Lee; 2012) For all k 3, E(X n E(X n )) k ( 1) k 2k k 2 ak n k 1. This implies that all moments larger than two of X n E(X n ) Var(Xn ) tend to infinity! Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
100 Higher Moments Theorem (Lee; 2012) For all k 3, E(X n E(X n )) k ( 1) k 2k k 2 ak n k 1. This implies that all moments larger than two of X n E(X n ) Var(Xn ) tend to infinity! Question: Is there a limit distribution? Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
101 Random Recursive Trees Unordered, rooted trees. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
102 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
103 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
104 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
105 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
106 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
107 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
108 Random Recursive Trees Unordered, rooted trees. Uniformly choose one of the nodes and attach a child. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
109 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
110 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
111 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
112 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
113 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
114 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
115 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
116 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
117 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
118 Cutting Down Random Recursive Trees Meir and Moon (1974): Randomly pick an edge and remove it; retain the tree containing the root. Y n = number of steps until tree is destroyed = number of edges cut = 4. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
119 Mean, Variance and Higher Moments Theorem (Panholzer; 2004) We have, and for k 2 E(Y n ) n log n E(Y n E(Y n )) k ( 1)k k(k 1) n k log k+1 n. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
120 Mean, Variance and Higher Moments Theorem (Panholzer; 2004) We have, and for k 2 E(Y n ) n log n E(Y n E(Y n )) k Thus, again the limit law of ( 1)k k(k 1) n k log k+1 n. Y n E(Y n ) Var(Yn ) cannot obtained from the method of moments! Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
121 Limit Law Theorem (Drmota, Iksanov, Moehle, Roessler; 2009) We have, with log 2 n n Y n log n log log n d Y E(e iλy ) = e iλ log λ π λ /2. The law of Y is spectrally negative stable with index of stability 1. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
122 Limit Law Theorem (Drmota, Iksanov, Moehle, Roessler; 2009) We have, with log 2 n n Y n log n log log n d Y E(e iλy ) = e iλ log λ π λ /2. The law of Y is spectrally negative stable with index of stability 1. Different proofs of this result exist. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
123 Limit Law of X n Theorem (Drmota, F., Lee; 2014) We have, X n E(X n ) Var(Xn )/2 d N(0, 1). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
124 Limit Law of X n Theorem (Drmota, F., Lee; 2014) We have, X n E(X n ) Var(Xn )/2 d N(0, 1). For the proof, we use singularity perturbation analysis. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
125 Limit Law of X n Theorem (Drmota, F., Lee; 2014) We have, X n E(X n ) Var(Xn )/2 d N(0, 1). For the proof, we use singularity perturbation analysis. A probabilistic proof explaining the curious normalization was given recently. S. Janson (2015). Maximal clades in random binary search trees, Electronic J. Combinatorics, 22:1, paper 31. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
126 Some Ideas of the Proof (i) Set X(y, z) = n 2 E ( e yxn) z n. Then, This is a Riccati DE. z z X(y, z) = X(y, z) + X2 (y, z) + e y z 2 2ey z 3 1 z. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
127 Some Ideas of the Proof (i) Set X(y, z) = n 2 E ( e yxn) z n. Then, This is a Riccati DE. z z X(y, z) = X(y, z) + X2 (y, z) + e y z 2 2ey z 3 1 z. Set Then, X(y, z) = X(y, z). z z X(y, z) = X 2 (y, z) + e y 1 + z 1 z. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
128 Some Ideas of the Proof (ii) Set Then, This is Whittaker s DE. X(y, z) = V (y, z) V (y, z). V (y, z) + e y 1 + z V (y, z) = 0. 1 z Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
129 Some Ideas of the Proof (ii) Set Then, This is Whittaker s DE. Solution is given by V (y, z) = M e y/2,1/2 X(y, z) = V (y, z) V (y, z). V (y, z) + e y 1 + z V (y, z) = 0. 1 z ( ) ( ) 2e y/2 (z 1) + c(y)w e y/2,1/2 2e y/2 (z 1), where c(y) = ( e y/2 1 ) M e y/2 +1,1/2 ( 2e y/2 ) ( W ) e y/2 +1,1/2 2e y/2. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
130 Some Ideas of the Proof (iii) Lemma V (y, z) is analytic in = {z C : z < 1 + δ}\ {branch cut from 1 to } for all y < η. Moreover, V (y, z) has only one (simple) zero with z 0 (y) = 1 ay + 2a 2 y 2 log y + O(y 2 ). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
131 Some Ideas of the Proof (iii) Lemma V (y, z) is analytic in = {z C : z < 1 + δ}\ {branch cut from 1 to } for all y < η. Moreover, V (y, z) has only one (simple) zero with z 0 (y) = 1 ay + 2a 2 y 2 log y + O(y 2 ). δ z 0 (y) 1 Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
132 Some Ideas of the Proof (iv) Let y = it/(2a n log n). Then, E ( e yxn) = 1 2πi C X(y, z) z n+1 dz. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
133 Some Ideas of the Proof (iv) Let y = it/(2a n log n). Then, E ( e yxn) = 1 2πi C X(y, z) z n+1 dz. Lemma We have, E ( e yxn) ( log = z 0 (y) n 3 ) n + O. n Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
134 Some Ideas of the Proof (iv) Let y = it/(2a n log n). Then, E ( e yxn) = 1 2πi C X(y, z) z n+1 dz. Lemma We have, E ( e yxn) ( log = z 0 (y) n 3 ) n + O. n This together with the expansion of z 0 (y) yields E ( e yxn) ( ) ( it n = exp 2 log n t2 1 + O 4 ( log log n log n )). Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
135 Summary Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
136 Summary We gave a comprehensive study of pattern occurrences in random phylogenetic trees. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
137 Summary We gave a comprehensive study of pattern occurrences in random phylogenetic trees. We explained the strong correlation between Shapley value and fair proportion index in phylogenetic trees. This is of great interest for people working in biodiversity. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
138 Summary We gave a comprehensive study of pattern occurrences in random phylogenetic trees. We explained the strong correlation between Shapley value and fair proportion index in phylogenetic trees. This is of great interest for people working in biodiversity. We found a curious central limit theorem for the number of groups formed by social animals under the neutral model. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
139 Summary We gave a comprehensive study of pattern occurrences in random phylogenetic trees. We explained the strong correlation between Shapley value and fair proportion index in phylogenetic trees. This is of great interest for people working in biodiversity. We found a curious central limit theorem for the number of groups formed by social animals under the neutral model. Analytic combinatorics is useful in studying mathematical problems for random phylogenetic trees. We expect many more applications. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
140 Summary We gave a comprehensive study of pattern occurrences in random phylogenetic trees. We explained the strong correlation between Shapley value and fair proportion index in phylogenetic trees. This is of great interest for people working in biodiversity. We found a curious central limit theorem for the number of groups formed by social animals under the neutral model. Analytic combinatorics is useful in studying mathematical problems for random phylogenetic trees. We expect many more applications. More input from biologists is needed to make our results more relevant from the point of view of applications. Michael Fuchs (NCTU) Phylogenetic Trees (PTs) June 30th, / 45
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