Multivariate analysis of genetic data: an introduction
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1 Multivariate analysis of genetic data: an introduction Thibaut Jombart MRC Centre for Outbreak Analysis and Modelling Imperial College London XXIV Simposio Internacional De Estadística Bogotá, 25th July /34
2 Outline Multivariate analysis in a nutshell Applications to genetic data Genetic diversity of pathogen populations 2/34
3 Outline Multivariate analysis in a nutshell Applications to genetic data Genetic diversity of pathogen populations 3/34
4 Multivariate data: some examples Association between individuals? Correlations between variables? 4/34
5 Multivariate data: some examples Association between individuals? Correlations between variables? 4/34
6 Multivariate analysis to summarize diversity 5/34
7 Multivariate analysis to summarize diversity 5/34
8 Multivariate analysis to summarize diversity 5/34
9 Multivariate analysis to summarize diversity 5/34
10 Multivariate analysis: an overview Multivariate analysis, a.k.a: dimension reduction techniques ordinations in reduced space factorial methods Purposes: summarize diversity amongst observations summarize correlations between variables 6/34
11 Multivariate analysis: an overview Multivariate analysis, a.k.a: dimension reduction techniques ordinations in reduced space factorial methods Purposes: summarize diversity amongst observations summarize correlations between variables 6/34
12 Most common methods Differences lie in input data: quantitative/binary variables: Principal Component Analysis (PCA) 2 categorical variables: Correspondance Analysis (CA) >2 categorical variables: Multiple Correspondance Analysis (MCA) Euclidean distance matrix: Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Many other methods for 2 data tables, spatial analysis, phylogenetic analysis, etc. 7/34
13 Most common methods Differences lie in input data: quantitative/binary variables: Principal Component Analysis (PCA) 2 categorical variables: Correspondance Analysis (CA) >2 categorical variables: Multiple Correspondance Analysis (MCA) Euclidean distance matrix: Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Many other methods for 2 data tables, spatial analysis, phylogenetic analysis, etc. 7/34
14 Most common methods Differences lie in input data: quantitative/binary variables: Principal Component Analysis (PCA) 2 categorical variables: Correspondance Analysis (CA) >2 categorical variables: Multiple Correspondance Analysis (MCA) Euclidean distance matrix: Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Many other methods for 2 data tables, spatial analysis, phylogenetic analysis, etc. 7/34
15 Most common methods Differences lie in input data: quantitative/binary variables: Principal Component Analysis (PCA) 2 categorical variables: Correspondance Analysis (CA) >2 categorical variables: Multiple Correspondance Analysis (MCA) Euclidean distance matrix: Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Many other methods for 2 data tables, spatial analysis, phylogenetic analysis, etc. 7/34
16 Most common methods Differences lie in input data: quantitative/binary variables: Principal Component Analysis (PCA) 2 categorical variables: Correspondance Analysis (CA) >2 categorical variables: Multiple Correspondance Analysis (MCA) Euclidean distance matrix: Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Many other methods for 2 data tables, spatial analysis, phylogenetic analysis, etc. 7/34
17 1 dimension, 2 dimensions, P dimensions Need to find most informative directions in a P-dimensional space. 8/34
18 1 dimension, 2 dimensions, P dimensions Need to find most informative directions in a P-dimensional space. 8/34
19 1 dimension, 2 dimensions, P dimensions Need to find most informative directions in a P-dimensional space. 8/34
20 Reducing P dimensions into 1 X R N P ; X = [x 1... x P ]: data matrix Q R P P metric in R P ; D R N N metric in R N u R P ; u = [u 1,..., u P ]: principal axis ( u 2 Q = 1) v R N ; v = XQu: principal component find u so that v 2 D is maximum. 9/34
21 Reducing P dimensions into 1 X R N P ; X = [x 1... x P ]: data matrix Q R P P metric in R P ; D R N N metric in R N u R P ; u = [u 1,..., u P ]: principal axis ( u 2 Q = 1) v R N ; v = XQu: principal component find u so that v 2 D is maximum. 9/34
22 Reducing P dimensions into 1 X R N P ; X = [x 1... x P ]: data matrix Q R P P metric in R P ; D R N N metric in R N u R P ; u = [u 1,..., u P ]: principal axis ( u 2 Q = 1) v R N ; v = XQu: principal component find u so that v 2 D is maximum. 9/34
23 Reducing P dimensions into 1 X R N P ; X = [x 1... x P ]: data matrix Q R P P metric in R P ; D R N N metric in R N u R P ; u = [u 1,..., u P ]: principal axis ( u 2 Q = 1) v R N ; v = XQu: principal component find u so that v 2 D is maximum. 9/34
24 Keeping more than one principal component u 1 and v 1 : 1st principal axis and component u 2 and v 2 : 2nd principal axis and component constraint: u 1 u 2 (i.e., u 1, u 2 Q = 0) find u 2 so that v 2 2 D is maximum 10/34
25 Keeping more than one principal component u 1 and v 1 : 1st principal axis and component u 2 and v 2 : 2nd principal axis and component constraint: u 1 u 2 (i.e., u 1, u 2 Q = 0) find u 2 so that v 2 2 D is maximum 10/34
26 Keeping more than one principal component u 1 and v 1 : 1st principal axis and component u 2 and v 2 : 2nd principal axis and component constraint: u 1 u 2 (i.e., u 1, u 2 Q = 0) find u 2 so that v 2 2 D is maximum 10/34
27 Keeping more than one principal component u 1 and v 1 : 1st principal axis and component u 2 and v 2 : 2nd principal axis and component constraint: u 1 u 2 (i.e., u 1, u 2 Q = 0) find u 2 so that v 2 2 D is maximum 10/34
28 How do we do this? Things that don t change: take u i the i-th eigenvector of the Q-symmetric matrix X T DXQ (alternatively) take v i the i-th eigenvector of the D-symmetric matrix XQX T D Things that change: pre-transformations of X (recoding, standardisation, etc.) metrics Q and D (implicitely distances in R P and R N ) most usual analyses are defined by (X, Q, D) 11/34
29 How do we do this? Things that don t change: take u i the i-th eigenvector of the Q-symmetric matrix X T DXQ (alternatively) take v i the i-th eigenvector of the D-symmetric matrix XQX T D Things that change: pre-transformations of X (recoding, standardisation, etc.) metrics Q and D (implicitely distances in R P and R N ) most usual analyses are defined by (X, Q, D) 11/34
30 Things that don t change: How do we do this? take u i the i-th eigenvector of the Q-symmetric matrix X T DXQ (alternatively) take v i the i-th eigenvector of the D-symmetric matrix XQX T D Things that change: pre-transformations of X (recoding, standardisation, etc.) metrics Q and D (implicitely distances in R P and R N ) most usual analyses are defined by (X, Q, D) packages: ade4, vegan 11/34
31 How many principal components to retain? Choice based on screeplot : barplot of eigenvalues Retain only significant structures... but not trivial ones. 12/34
32 Outputs of multivariate analyses: an overview Main outputs: principal components: diversity amongst individuals principal axes: nature of the structures eigenvalues: magnitude of structures 13/34
33 Outputs of multivariate analyses: an overview Main outputs: principal components: diversity amongst individuals principal axes: nature of the structures eigenvalues: magnitude of structures 13/34
34 Outputs of multivariate analyses: an overview Main outputs: principal components: diversity amongst individuals principal axes: nature of the structures eigenvalues: magnitude of structures 13/34
35 Usual summary of an analysis: the biplot Biplot: principal components (points) + loadings (arrows) groups of individuals structuring variables (longest arrows) magnitude of the structures 14/34
36 Multivariate analysis in a nutshell variety of methods for different types of variables principal components (PCs) summarize diversity variable loadings identify discriminating variables other uses of PCs: maps (spatial structures), models (response variables or predictors),... 15/34
37 Multivariate analysis in a nutshell variety of methods for different types of variables principal components (PCs) summarize diversity variable loadings identify discriminating variables other uses of PCs: maps (spatial structures), models (response variables or predictors),... 15/34
38 Multivariate analysis in a nutshell variety of methods for different types of variables principal components (PCs) summarize diversity variable loadings identify discriminating variables other uses of PCs: maps (spatial structures), models (response variables or predictors),... 15/34
39 Multivariate analysis in a nutshell variety of methods for different types of variables principal components (PCs) summarize diversity variable loadings identify discriminating variables other uses of PCs: maps (spatial structures), models (response variables or predictors),... 15/34
40 Outline Multivariate analysis in a nutshell Applications to genetic data Genetic diversity of pathogen populations 16/34
41 From DNA sequences to patterns of biological diversity 17/34
42 From DNA sequences to patterns of biological diversity 17/34
43 From DNA sequences to patterns of biological diversity 17/34
44 From DNA sequences to patterns of biological diversity 17/34
45 From DNA sequences to patterns of biological diversity 17/34
46 From DNA sequences to patterns of biological diversity 17/34
47 From DNA sequences to patterns of biological diversity 17/34
48 From DNA sequences to patterns of biological diversity 17/34
49 From DNA sequences to patterns of biological diversity 17/34
50 DNA sequences: a rich source of information hundreds/thousands individuals up to millions of single nucleotide polymorphism (SNPs) more generally, most genetic data can be treated as frequencies Multivariate analysis use to summarize genetic diversity. 18/34
51 DNA sequences: a rich source of information hundreds/thousands individuals up to millions of single nucleotide polymorphism (SNPs) more generally, most genetic data can be treated as frequencies Multivariate analysis use to summarize genetic diversity. 18/34
52 DNA sequences: a rich source of information hundreds/thousands individuals up to millions of single nucleotide polymorphism (SNPs) more generally, most genetic data can be treated as frequencies Multivariate analysis use to summarize genetic diversity. 18/34
53 DNA sequences: a rich source of information hundreds/thousands individuals up to millions of single nucleotide polymorphism (SNPs) more generally, most genetic data can be treated as frequencies Multivariate analysis use to summarize genetic diversity. 18/34
54 First application of multivariate analysis in genetics PCA of genetic data, native human populations (Cavalli-Sforza 1966, Proc B) First 2 principal components separate populations into continents. 19/34
55 First application of multivariate analysis in genetics PCA of genetic data, native human populations (Cavalli-Sforza 1966, Proc B) First 2 principal components separate populations into continents. 19/34
56 Applications: some examples PCA of genetic data + colored maps of principal components (Cavalli-Sforza et al. 1993, Science) Signatures of Human expansion out-of-africa. 20/34
57 Since then... Multivariate methods used in genetics Principal Component Analysis (PCA) Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Correspondance Analysis (CA) Discriminant Analysis (DA) Canonical Correlation Analysis (CCA)... 21/34
58 Since then... Multivariate methods used in genetics Principal Component Analysis (PCA) Principal Coordinates Analysis (PCoA) / Metric Multidimensional Scaling (MDS) Correspondance Analysis (CA) Discriminant Analysis (DA) Canonical Correlation Analysis (CCA)... packages: adegenet, ade4, pegas 21/34
59 Since then... Applications reveal spatial structures (historical spread) explore genetic diversity identify cryptic species discover genotype-phenotype association... review in Jombart et al. 2009, Heredity 102: Applications in genetics of pathogen populations. 22/34
60 Since then... Applications reveal spatial structures (historical spread) explore genetic diversity identify cryptic species discover genotype-phenotype association... review in Jombart et al. 2009, Heredity 102: Applications in genetics of pathogen populations. 22/34
61 Outline Multivariate analysis in a nutshell Applications to genetic data Genetic diversity of pathogen populations 23/34
62 Why investigate the diversity of pathogen populations? Genetic data: increasingly important in infectious disease epidemiology Purposes classify pathogens, describe their relationships assess the spatio-temporal dynamics of infectious diseases reconstruct epidemiological processes (transmission) 24/34
63 Why investigate the diversity of pathogen populations? Genetic data: increasingly important in infectious disease epidemiology Purposes classify pathogens, describe their relationships assess the spatio-temporal dynamics of infectious diseases reconstruct epidemiological processes (transmission) 24/34
64 Why investigate the diversity of pathogen populations? Genetic data: increasingly important in infectious disease epidemiology Purposes classify pathogens, describe their relationships assess the spatio-temporal dynamics of infectious diseases reconstruct epidemiological processes (transmission) 24/34
65 Why investigate the diversity of pathogen populations? Genetic data: increasingly important in infectious disease epidemiology Purposes classify pathogens, describe their relationships assess the spatio-temporal dynamics of infectious diseases reconstruct epidemiological processes (transmission) 24/34
66 Different questions at different scales Where and how can multivariate analysis of pathogen genetic data be useful? 25/34
67 Different questions at different scales Where and how can multivariate analysis of pathogen genetic data be useful? 25/34
68 Describing pathogen populations Population genetics: identify populations of organisms and describe their relationships What is a population? Usual definition: set of organisms mating at random Problem: no mating in most pathogens (e.g. viruses, bacteria) Genetic clusters: set of genetically related pathogens (e.g. same outbreak, same epidemic). aim: identify and describe genetic clusters 26/34
69 Describing pathogen populations Population genetics: identify populations of organisms and describe their relationships What is a population? Usual definition: set of organisms mating at random Problem: no mating in most pathogens (e.g. viruses, bacteria) Genetic clusters: set of genetically related pathogens (e.g. same outbreak, same epidemic). aim: identify and describe genetic clusters 26/34
70 Describing pathogen populations Population genetics: identify populations of organisms and describe their relationships What is a population? Usual definition: set of organisms mating at random Problem: no mating in most pathogens (e.g. viruses, bacteria) Genetic clusters: set of genetically related pathogens (e.g. same outbreak, same epidemic). aim: identify and describe genetic clusters 26/34
71 Describing pathogen populations Population genetics: identify populations of organisms and describe their relationships What is a population? Usual definition: set of organisms mating at random Problem: no mating in most pathogens (e.g. viruses, bacteria) Genetic clusters: set of genetically related pathogens (e.g. same outbreak, same epidemic). aim: identify and describe genetic clusters 26/34
72 Describing pathogen populations Population genetics: identify populations of organisms and describe their relationships What is a population? Usual definition: set of organisms mating at random Problem: no mating in most pathogens (e.g. viruses, bacteria) Genetic clusters: set of genetically related pathogens (e.g. same outbreak, same epidemic). aim: identify and describe genetic clusters 26/34
73 Genetic clustering using K-means & BIC (Jombart et al. 2010, BMC Genetics) Variance partitioning model (ANOVA): tot. variance = (bet. groups) + (wit. groups) Performances: K-means STRUCTURE on simulated data (various island and stepping stone models) orders of magnitude faster (seconds vs hours/days) 27/34
74 Genetic clustering using K-means & BIC (Jombart et al. 2010, BMC Genetics) Variance partitioning model (ANOVA): tot. variance = (bet. groups) + (wit. groups) Performances: K-means STRUCTURE on simulated data (various island and stepping stone models) orders of magnitude faster (seconds vs hours/days) 27/34
75 Genetic clustering using K-means & BIC (Jombart et al. 2010, BMC Genetics) Variance partitioning model (ANOVA): tot. variance = (bet. groups) + (wit. groups) Performances: K-means STRUCTURE on simulated data (various island and stepping stone models) orders of magnitude faster (seconds vs hours/days) package: adegenet, function find.clusters 27/34
76 PCA of seasonal influenza (A/H3N2) data Data: seasonal influenza (A/H3N2), 500 HA segments. Little temporal evolution, burst of diversity in 2002?? 28/34
77 PCA of seasonal influenza (A/H3N2) data Data: seasonal influenza (A/H3N2), 500 HA segments. Little temporal evolution, burst of diversity in 2002?? 28/34
78 Which diversity to represent? Total diversity not relevant to analyse clusters. Discriminant Analysis of Principal Components (DAPC): (Jombart et al. 2010, BMC Genetics) maximizes group discrimination ( between/within ratio) provides group membership probabilities (prediction possible) as computer-efficient as PCA 29/34
79 Which diversity to represent? Total diversity not relevant to analyse clusters. Discriminant Analysis of Principal Components (DAPC): (Jombart et al. 2010, BMC Genetics) maximizes group discrimination ( between/within ratio) provides group membership probabilities (prediction possible) as computer-efficient as PCA 29/34
80 Which diversity to represent? Total diversity not relevant to analyse clusters. Discriminant Analysis of Principal Components (DAPC): (Jombart et al. 2010, BMC Genetics) maximizes group discrimination ( between/within ratio) provides group membership probabilities (prediction possible) as computer-efficient as PCA package: adegenet, function dapc 29/34
81 DAPC of seasonal influenza (A/H3N2) data Strong temporal signal, originality of 2006 isolates (new alleles). 30/34
82 DAPC of seasonal influenza (A/H3N2) data Strong temporal signal, originality of 2006 isolates (new alleles). 30/34
83 Identifying antigenic clusters in influenza (A/H3N2) Antigenic clusters identified directly from AA sequences. 31/34
84 Identifying antigenic clusters in influenza (A/H3N2) Antigenic clusters identified directly from AA sequences. 31/34
85 DAPC to identify structuring alleles DAPC finds combinations of alleles most differing between groups. Simulated data: (Jombart & Ahmed 2011, Bioinformatics) 2 clusters, 50 isolates each 1,000,000 non structured SNPs 1,000 structured SNPs (i.e. different frequencies between groups) Possible applications to pathogen GWAS (e.g. SNPs related to antibiotic resistance in bacteria). 32/34
86 DAPC to identify structuring alleles DAPC finds combinations of alleles most differing between groups. Simulated data: (Jombart & Ahmed 2011, Bioinformatics) 2 clusters, 50 isolates each 1,000,000 non structured SNPs 1,000 structured SNPs (i.e. different frequencies between groups) Possible applications to pathogen GWAS (e.g. SNPs related to antibiotic resistance in bacteria). 32/34
87 Limits of multivariate analysis Methicillin-resistant Staphylococcus aureus (MRSA) outbreak within hospital, Thailand. 200 full-genome sequences. 1, 000 SNPs. Observations: greater diversity than expected genetic clusters can be defined transmissions at within-cluster level multivariate analysis = loss of information 33/34
88 Limits of multivariate analysis Methicillin-resistant Staphylococcus aureus (MRSA) outbreak within hospital, Thailand. 200 full-genome sequences. 1, 000 SNPs. Observations: greater diversity than expected genetic clusters can be defined transmissions at within-cluster level multivariate analysis = loss of information 33/34
89 Limits of multivariate analysis Methicillin-resistant Staphylococcus aureus (MRSA) outbreak within hospital, Thailand. 200 full-genome sequences. 1, 000 SNPs. Observations: greater diversity than expected genetic clusters can be defined transmissions at within-cluster level multivariate analysis = loss of information 33/34
90 Limits of multivariate analysis Methicillin-resistant Staphylococcus aureus (MRSA) outbreak within hospital, Thailand. 200 full-genome sequences. 1, 000 SNPs. Observations: greater diversity than expected genetic clusters can be defined transmissions at within-cluster level multivariate analysis = loss of information 33/34
91 Limits of multivariate analysis Methicillin-resistant Staphylococcus aureus (MRSA) outbreak within hospital, Thailand. 200 full-genome sequences. 1, 000 SNPs. Observations: greater diversity than expected genetic clusters can be defined transmissions at within-cluster level multivariate analysis = loss of information Multivariate analysis usually not informative on small-scale processes. 33/34
92 Summary multivariate analysis used for 50 years in genetics, still an active field for methodological development increasingly useful as datasets grow specific applications to pathogen genetic data limits reached when reconstructing fine-scale processes more at: 34/34
93 Summary multivariate analysis used for 50 years in genetics, still an active field for methodological development increasingly useful as datasets grow specific applications to pathogen genetic data limits reached when reconstructing fine-scale processes more at: 34/34
94 Summary multivariate analysis used for 50 years in genetics, still an active field for methodological development increasingly useful as datasets grow specific applications to pathogen genetic data limits reached when reconstructing fine-scale processes more at: 34/34
95 Summary multivariate analysis used for 50 years in genetics, still an active field for methodological development increasingly useful as datasets grow specific applications to pathogen genetic data limits reached when reconstructing fine-scale processes more at: 34/34
96 Summary multivariate analysis used for 50 years in genetics, still an active field for methodological development increasingly useful as datasets grow specific applications to pathogen genetic data limits reached when reconstructing fine-scale processes more at: 34/34
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