Anatomical and molecular data of Crassostrea collected on Rio Grande do Sul, Brazil

The molecular data is in .txt format:

You must convert the sequence data in .fasta using the Bioedit program version 7.2.5

Now, you open the file in Mega11 program to conduct the molecular analysis

>Choose Analyse> Nucleotide Sequences> Confirm that data corresponds to Protein-coding nucleotide sequence data

Click on Phylogeny> Construct Neighbor-Joining tree and apply the following configurations before the construction of the tree:

Bootstrap method = 1000 replicates

Method = Kimura 2-parameter model

 

After constructing the Neighbor-Joining tree, right-click on the branch clade containing both Saccostrea and Ostrea sequences and click on the option Root the clade

 

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The anatomical data was converted from an Excel archive named "Data.xlsx".

You need to convert the pdf data into Excel data prior its usage on Rstudio for statistical analysis.

After the Excel data is imported into Rstudio, apply the following steps on the Console channel to conduct the same statistical analysis provided for "Identity of Crassostrea praia (Ihering, 1907) and other oysters from Rio Grande do Sul, Brazil":

 

#packages used#

 

library("FSA")

library("rstatix")

require("agricolae")

library("ggplot2")

library("qqplotr")

library("dplyr")

library("DescTools")

library("FSA")

library("PMCMRplus")

library("vcd")

library("ggstatsplot")

library("MKinfer")

library("ggplot2")

 

#data summon#

 

library(readxl)

Data <- read_excel("cap2 doc/Para submissão/Data.xlsx")

View(Data)

 

#continuous data#

 

#Mantle Height#

 

Mant_h <- aov(Data$Mantle_height ~ Data$Species)

summary(Mant_h)

 

shapiro.test(residuals(Mant_h))

bartlett.test(Data$Mantle_height ~ Data$Species)

perm.t.test(Mantle_height ~Species, data = Data)

 

 

 

#Mantle length#

 

Mant_l <- aov(Data$Mantle_length ~ Data$Species)

summary(Mant_l)

 

shapiro.test(residuals(Mant_l))

bartlett.test(Data$Mantle_length ~ Data$Species)

perm.t.test(Mantle_length ~Species, data = Data)

 

 

 

#Labial palps#

 

LP <- aov(Data$Labial_palps_height ~ Data$Species)

summary(LP)

 

shapiro.test(residuals(LP))

bartlett.test(Data$Labial_palps_height ~ Data$Species)

perm.t.test(Labial_palps_height ~Species, data = Data)

 

#Anus format#

 

chisq.test(Data$Anus_format, Data$Species)

ggbarstats(data = Data, x = Anus_format, y = Species, 

           bf.message = FALSE)

 

 

#Accessory heart form#

 

chisq.test(Data$Accessory_heart, Data$Species)

ggbarstats(data = Data, x = Accessory_heart, y = Species, 

           bf.message = FALSE)

 

#pattern of tentacles of middle mantle margin#

 

chisq.test(Data$Pattern_of_middle_mantle_tentacles, Data$Species)

ggbarstats(data = Data, x = Pattern_of_middle_mantle_tentacles, y = Species, 

           bf.message = FALSE)

 

#Correlation between ventricle, atrium formats and species#

Heart = mosaic(~ Species + Heart_ventricle_format + Heart_atrium_format, 

               data = Data,

               shade = TRUE, 

               labeling_args = list(rot_labels = c(right = 0), 

                                                  set_varnames = c(Heart_ventricle_format = "Ventricle",

                                                                   Heart_atrium_format = "Atrium"), 

                                                  abbreviate_labs = c(Heart_atrium_format = 3)), 

               gp_labels = gpar(fontsize = 10, fontface = 3))