ChiLiubio
commented
9 months ago Hi. I used the example data to test the steps and found it is fine. So if an issue is still there, please make the plot using ggplot2 by extracting the rarefying data results, i.e. t1$res_rarefy.
The following is my test steps.
library(microeco)
library(mecodev)
data(sample_info_16S)
data(otu_table_16S)
set.seed(123)
dataset <- microtable$new(sample_table = sample_info_16S, otu_table = otu_table_16S)
dataset$tidy_dataset()
# trans_rarefy class
t1 <- trans_rarefy$new(dataset, alphadiv = "Observed", depth = c(0, 10, 50, 500, 2000, 4000, 6000, 8000))
g1 <- t1$plot_rarefy(color = "Group", show_point = FALSE, add_fitting = FALSE)
g1 + facet_wrap(~ Group, nrow = 1) + theme(
panel.background = element_rect(fill = "transparent", color = NA),
plot.background = element_rect(fill = "transparent", color = NA),
text = element_text(size = 20) # Set the desired font size for all text elements
)
apoosakkannu
Hi, I have problem in getting desired levels of a factor in facet_wrap of rarefaction curve even after i changed the levels of factor in dataset itself, the code is following,
upload the required data for pacbio 16S rRNA gene amplicon analysis
creation and cleanup of dataset object for downstream analyses
set the seed for reproducibility of some of the random analysis below
set.seed(123)
upload all the data files
upload the OTU abundance file
abund_table<-read.csv("/scratch/project_2003832/pacbio_rawdata/bird_bat/asv_stat/all_decontam_asv/samples_asv_abundtable_decontam_all.csv",row.names=1,check.names=FALSE)
upload the meta data file
meta_table<-read.csv("/scratch/project_2003832/pacbio_rawdata/bird_bat/asv_stat/all_decontam_asv/samples_asv_metatable_20221213.csv",row.names=1,check.names=FALSE)
load the taxonomy
OTU_taxonomy<-read.csv("/scratch/project_2003832/pacbio_rawdata/bird_bat/asv_stat/all_decontam_asv/samples_asv_taxtable_decontam_all.csv",row.names=1,check.names=FALSE)
make the taxonomic information unified, very important #require "tidytree" and "microeco" packages
library(tidytree) #version 0.3.5 library(microeco) #version 0.11.0
OTU_taxonomy %<>% tidy_taxonomy
load the representative sequences ##require biostrings package
library(Biostrings) rep_seq<-readDNAStringSet(filepath="/scratch/project_2003832/pacbio_rawdata/bird_bat/asv_stat/all_nondecontam_asv/samples_asv_repseq.fasta", format="fasta", nrec=-1L, skip=0L, seek.first.rec=FALSE, use.names=TRUE)
create and cleanup data object #The packages "microeco" and "tidytree" uploaded already
create a dataset of all the data uploaded
dataset <- microtable$new(sample_table = meta_table, otu_table = abund_table, tax_table = OTU_taxonomy, rep_fasta = rep_seq, auto_tidy = TRUE)
make the OTU and sample information consistent across all files in the dataset object
dataset$tidy_dataset()
dataset
remove control
remember first clone the full dataset
dataset1 <- clone(dataset) dataset1$sample_table <- subset(dataset1$sample_table, Host_taxa != "Control")
make the object tidy
dataset1$tidy_dataset() dataset1$tidy_dataset()
dataset1
str(dataset1$sample_table$Host_taxa)
Convert a character variable to a factor
Change the levels of the Host_taxa factor variable
dataset1$sample_table$Host_taxa <- factor( dataset1$sample_table$Host_taxa, levels = c("Black bird", "Mallard", "Wood pigeon", "Nathusius bat") )
rarefaction analysis
library(microeco) library(mecodev) library(ggplot2)
set.seed is used to fix the random number generation to make the results repeatable
set.seed(123)
trans_rarefy class
t1 <- trans_rarefy$new(dataset1, alphadiv = "Observed", depth = c(0, 10, 50, 500, 1000))
Change the levels of the Host_taxa factor variable
t1$dataset$sample_table$Host_taxa <- factor( t1$dataset$sample_table$Host_taxa, levels = c("Black bird", "Mallard", "Wood pigeon", "Nathusius bat") )
library(ggplot2)
rarefaction <- t1$plot_rarefy(color = "Host_taxa", show_point = FALSE, add_fitting = FALSE) + facet_wrap(~Host_taxa, nrow = 1) + theme( panel.background = element_rect(fill = "transparent", color = NA), plot.background = element_rect(fill = "transparent", color = NA), text = element_text(size = 20) # Set the desired font size for all text elements )
Display the plot
print(rarefaction)