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Taller en Análisis filogenéticos comparativos en Ecofisiología. A plicación 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 )

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taller en an lisis filogen ticos comparativos en ecofisiolog a

Taller en Análisisfilogenéticoscomparativos en Ecofisiología

Aplicación de Mesquite y R

programa
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)
programa1
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
Goals of Comparative Analyses
  • Investigar la evolucióncarácter
  • La coevolución de caracteres
  • Control de la no independencia de lasespecies
  • Hipótesis de ensayo de adaptación
estad sticas tradicionales de asumir la independencia de las especies unidades de muestreo
Estadísticastradicionales de asumir la independencia de lasespecies (unidades de muestreo)…
slide6

Pero, lasespeciesexhibendiferentesniveles de relación, queafecta a lasinferencias de la adaptación local y la diversificación

Pearman et al. TREE 2008

estimaci n car cter ancestral
Estimacióncarácter ancestral

Garganta Morphs en Urosaurus

Feldman et al. 2011. Molecular Phylogenetics and Evolution

dos importantes programas
Dos importantesprogramas

http://mesquiteproject.org/mesquite/mesquite.html

http://cran.r-project.org/

objetivos de mesquite
Objetivos de Mesquite
  • Manipulate Phylogenetic Trees
    • Estimate Ancestral Character States
    • Estimate Character Correlations
    • Inferences of Character Evolution
    • Multivariate Analyses
objetivos para
Objetivospara
  • How to use R to Manipulate Data
  • Phylogenetic Comparative Analysis
  • Statistical Analyses not available in Mesquite
ventajas de
Ventajas de
  • Free
  • Many packages available
  • Powerful and Flexible
  • Platform Independent
    • MacOS
    • Linux
    • Windows
r studio a gui for
R Studio – A GUI for

http://www.rstudio.com/

37 paquetes filogen ticos en
37 Paquetesfilogenéticos en
  • ape
  • caper
  • geiger
  • motmot
  • OUwie
  • phylobase
  • phyloclim
  • phytools
  • picante

Sólovoy a describirestospaquetes

d atos n ecesarios
DatosNecesarios
  • 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 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
A tutorial in Mesquite

1. Characters

2. Taxa

3. Trees

Treselementos de Mesquite

primero ventana de mesquite
PrimeroVentana de Mesquite

Projects and Files – list of open projects

Log – list of commands

numero de caracteres
Numero de caracteres

y el tipo de caracteres

dataframes
Dataframes
  • Rectangular table of information
    • Can include numbers, text
  • This is the form of your data when you import into R
slide44

Morphology =

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

importaci n de datos
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
slide47

Species SVL Mass CS RCM EggMEggSEggVOffSVLAdSAgeMatEnv

Amblyrhynchus_cristatus 279.0 1370.0 2.6 0.18 98.6 90.33 21.8 NA 0.85 41.0 Island

Conolophus_pallidus 440.0 4300.0 10.0 NA NA NA NA NA NA NA Island

Conolophus_subcristatus 415.0 3600.0 13.5 0.199 51.2 63.4 NA NA 0.9 84.0 Island

Ctenosaura_clarki 126.58 70.78 8.5 0.24 2.45 23.37 3.05 NA NA NA Main

Ctenosaura_hemilopha 219.33 375.0 27.33 0.21 2.37 21.22 2.69 NA NA NA Main

Ctenosaura_pectinata 238.7 482.0 28.0 0.23 3.92 26.3 2.29 NA NA NA Main

Ctenosaura_similis 238.39 795.13 31.1 0.4 7.72 30.92 2.28 NA 0.78 22.0 Main

Cyclura_carinata 225.0 605.3 5.1 0.21 25.0 52.0 44.87 NA 0.9 72.0 Island

Cyclura_ricordi 355.0 1275.0 10.2 NA NA NA NA NA NA NA Island

Cyclura_cychlura 405.0 2805.0 8.75 0.21 68.69 73.01 61.34 96.0 NA NA Island

Cyclura_nubila 340.0 1700.0 8.12 NA NA NA NA 99.8 NA NA Island

Cyclura_cornuta 355.0 3745.6 15.76 NA NA NA NA NA 0.9 72.0 Island

Cyclura_inornata 320.0 1336.0 4.1 0.165 55.12 66.0 NA 95.0 NA 132.0 Island

Cyclura_stejnegeri 475.0 4516.0 2.4 0.06 115.0 81.66 122.45 NA NA 110.0 Island

Dipsosaurus_dorsalis 123.0 70.0 5.6 NA NA NA NA NA 0.66 32.0 Main

Iguana_iguana 360.35 115.65 32.86 0.46 15.7 39.35 NA NA NA NA Main

Sauromalus_obesus 160.55 180.0 8.59 0.38 8.0 25.0 15.0 NA 0.8 48.0 Main

Sauromalus_hispidus 279.0 900.0 22.2 0.24 10.0 25.0 24.0 NA NA NA Island

Sauromalus_varius 293.6 1200.0 23.4 0.35 18.0 40.0 28.0 NA NA NA Island

Crotaphytus_collaris 84.8 24.66 8.6 0.217 1.23 21.3 NA NA 0.48 12.0 Main

importing data
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
Otrasformas de importardatos
  • 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
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)
f altan d atos
FaltanDatos
  • 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 m todos filogen ticos en r
Los métodosfilogenéticos 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
manana

Manana

Tree Selection

Ancestor Character State Estimation