Transcript Workshop in Phylogenetic Analyses in Ecophysiology

Taller en Análisis filogenéticos
comparativos en Ecofisiología
Aplicación de Mesquite y R
Programa
• Primero (11 Diciembre)
• Introduction to Mesquite and R
• Data Preparation and Manipulation
• Tardes – Practical Use of Mesquite y R
• Segundo (12 Diciembre)
• Selection of Phylogenetic Trees
• Supertrees – Assemblying Composite Trees
– Sources of Phylogenetic Hypotheses
• Estimation of Ancestral Character States
– Categorical (Mesquite & R)
– Continuous (R – ace)
Programa
• Tercera (13 Diciembre)
– Estimation of Phylogenetic Signal
– Statistical Methods incorporating Phylogenetic
information
• Phylogenetic Independent Contrasts
• Phylogenetic GLS
• Multivariate Analyses
– Bring your own Data!
Goals of Comparative Analyses
• Investigar la evolución carácter
• La coevolución de caracteres
• Control de la no independencia de las
especies
• Hipótesis de ensayo de adaptación
Estadísticas tradicionales de asumir la
independencia de las especies
(unidades de muestreo)…
Pero, las especies exhiben diferentes niveles de
relación, que afecta a las inferencias de la adaptación
local y la diversificación
Pearman et al. TREE 2008
Estimación carácter ancestral
Garganta Morphs en Urosaurus
Feldman et al. 2011. Molecular Phylogenetics and Evolution
Dos importantes programas
http://mesquiteproject.org/mesquite/mesquite.html
http://cran.r-project.org/
Objetivos de Mesquite
• Manipulate Phylogenetic Trees
– Estimate Ancestral Character States
– Estimate Character Correlations
– Inferences of Character Evolution
– Multivariate Analyses
Objetivos para
• How to use R to Manipulate Data
• Phylogenetic Comparative Analysis
• Statistical Analyses not available in Mesquite
Ventajas de
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Free
Many packages available
Powerful and Flexible
Platform Independent
– MacOS
– Linux
– Windows
Página de Inicio para R
Console de
R Studio – A GUI for
http://www.rstudio.com/
37 Paquetes filogenéticos en
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ape
caper
geiger
motmot
OUwie
phylobase
phyloclim
phytools
picante
Sólo voy a describir estos paquetes
Datos Necesarios
• Phylogenetic Tree
– NEXUS format
– NEWICK format ((B:0.2,(C:0.3,D:0.4)E:0.5)F:0.1)A
• Data
– Continuous
– Discrete
– Flat Format (Texto, ASCII)
Nexus Data File Format
#nexus
...
begin trees;
translate
1 Phrynosoma,
2 Uta,
3 Petrosaurus,
4 Urosaurus,
5 Sceloporus
;
tree one = [&U] (1,2,(3,(4,5));
tree two = [&U] (1,3,(5,(2,4));
end;
A tutorial in Mesquite
• Tres elementos de Mesquite
1. Characters
2. Taxa
3. Trees
Primero Ventana de Mesquite
Projects and Files – list of
open projects
Log – list of commands
Crear un nuevo proyecto (archivo)
Nuevas opciones de archivo
Numero de caracteres
y el tipo de caracteres
Ventana de caracteres
Podemos anotar caracteres
“Show Annotations Panel”
MetaData
Taxa se pueden asignar a grupos
Los árboles pueden establecerse en
diferentes formas
Taxa ventana
Select a Tree
Ver el árbol
Total del Proyecto
A Gentle Introduction to
El intérprete interactivo, R
Asigna variables
Types of Variables
Mode
Numeric
Character
Factor
Logical
Example
10.2, 20
“Morph”, “Substrate”
Categorical
TRUE, FALSE, T, F
Operators
Functions
Combinando elementos en matrices
Matrices
Operations on Matrices
Dataframes
• Rectangular table of information
– Can include numbers, text
• This is the form of your data when you import
into R
Character 1 Character 2
Species 1
Species 2
Species 3
Character 3
Character 4
Character 5
Morphology =
Character1
Character2
Character3
Character4
Character5
Species1
22.1
100.3
15
2.2
22
Species2
23.7
125.0
17.6
3.8
25
Species3
35.2
98.3
22.1
1.9
19
Dataframe Behaves as a Matrix
No Spaces in species or variable names
Use attach to directly refer to variable names
attach(morphology)
Character 2 # gives all values of character 2
Use Help
Importación de datos
• Change the “working directory”
– Easy in R Studio
• Data should be in a clean rectangular matrix
– Flat File (No formatting), ASCII text
– Exported from excel
• First row: Variable Names
• First column: Species/Taxon Names
• example: Iguana Life History Data
Species
Amblyrhynchus_cristatus
Conolophus_pallidus
Conolophus_subcristatus
Ctenosaura_clarki
Ctenosaura_hemilopha
Ctenosaura_pectinata
Ctenosaura_similis
Cyclura_carinata
Cyclura_ricordi
Cyclura_cychlura
Cyclura_nubila
Cyclura_cornuta
Cyclura_inornata
Cyclura_stejnegeri
Dipsosaurus_dorsalis
Iguana_iguana
Sauromalus_obesus
Sauromalus_hispidus
Sauromalus_varius
Crotaphytus_collaris
SVL
Mass
279.0 1370.0
2.6
440.0 4300.0 10.0
415.0 3600.0 13.5
126.58
70.78 8.5
219.33
375.0 27.33
238.7
482.0 28.0
238.39
795.13 31.1
225.0
605.3
5.1
355.0
1275.0 10.2
405.0
2805.0
8.75
340.0
1700.0
8.12
355.0
3745.6 15.76
320.0
1336.0
4.1
475.0
4516.0
2.4
123.0
70.0
5.6
360.35
115.65 32.86
160.55
180.0
8.59
279.0
900.0 22.2
293.6
1200.0 23.4
84.8
24.66 8.6
CS
RCM
EggM
EggS
EggV
OffSVL
0.18
98.6
90.33 21.8
NA
0.85
NA
NA
NA
NA
NA
NA
0.199 51.2 63.4
NA
NA
0.9
0.24
2.45 23.37
3.05
NA
NA
0.21
2.37 21.22
2.69
NA
NA
0.23
3.92 26.3
2.29
NA
NA
0.4
7.72 30.92
2.28
NA
0.78
0.21 25.0
52.0
44.87
NA
0.9
NA
NA
NA
NA
NA
NA
0.21 68.69 73.01 61.34
96.0
NA
NA
NA
NA
NA
99.8
NA
NA
NA
NA
NA
NA
0.9
0.165 55.12 66.0
NA
95.0
NA
0.06 115.0 81.66 122.45
NA
NA
NA
NA
NA
NA
NA
0.66
0.46
15.7 39.35
NA
NA
NA
0.38
8.0
25.0
15.0
NA
0.8
0.24 10.0
25.0
24.0
NA
NA
0.35 18.0
40.0
28.0
NA
NA
0.217 1.23 21.3
NA
NA
0.48
AdS
AgeMat Env
41.0
Island
NA
Island
84.0
Island
NA
Main
NA
Main
NA
Main
22.0
Main
72.0
Island
NA
Island
NA
Island
NA
Island
72.0
Island
132.0 Island
110.0 Island
32.0
Main
NA
Main
48.0
Main
NA
Island
NA
Island
12.0
Main
Importing Data
• Workhorse function: read.table()
iguana.lh <- read.table(file=“iguanalh.txt”, header=TRUE)
• iguana.lh (dataframe name)
• Check to make sure data were read in correctly
• iguana.lh[1:10,] # look at first 10 rows
Otras formas de importar datos
• Other formats: read.csv(), read.delim()
• (useful if there are spaces within some fields)
• Handy function:
file.choose() # navigate to file
iguana.lh <- read.table(file=file.choose(), header=T)
attach(iguana.lh) # easy to manipulate variables
Factors
• Used to represent categorical data; by default,
read.table()
• converts columns with characters into factors
• Factors look like strings, but are treated differently by
functions
• species # example of a factor
• Factors have levels, which are the unique values it takes
• levels(species) # example of a factor
• Factor levels may be ordered (e.g., low, med, high), which is
important in some analyses (see ?factor and ?ordered)
Faltan Datos
• Represented by a special character in R: NA
• Many functions have an argument na.rm
– If TRUE, NA’s are removed
– FALSE is usually default (function returns NA)
– median(x, na.rm=TRUE)
• read.table(file, na.strings=“NA”)
• na.strings=“-999” # here, missing data are -999
• • Useful function: complete.cases(iguanlh.txt)
• • Returns logical vector, T for rows without missing data
• cc<- complete.cases(iguanalh.txt)
Los métodos filogenéticos en R
• Start with a Tree
• library(ape) # load ape package
• We can create a random tree:
• tree <- rtree(25) # 25 terminal taxa
• Normally, read in tree(s) from file
• tree <- read.nexus(file) # Nexus format
• tree <- read.tree(file, …) # Newick format
Tree Structure in ape
Plot a Tree
View a Tree
Manana
Tree Selection
Ancestor Character State Estimation