qiime2R visualization error message

Community Plugin Support
21

Hi Chris,

Could you email me a copy of your metadata and I will take a look at where it could be going wrong? I assume it has something to do with the header lines. In the meanwhile you can use read_tsv to import your data, and parameter skip=1 if you need to remove a definition line.
If I am understanding your second question properly, I would recommend having only one massive metadata file, In the example you posted, I think I used group as a generic term, but it could be any column in your metadata file, you don’t need to create a new file with only two columns.

Jordan

22

OK, so your solution works well–thanks!

I just exported the data (in this case family-level data) as csv from the barplot in qiime2view and ran your code. I did not realize that the barplot csv file exported from qiime2 in qiime2view would already have the metadata incorporated in the csv file at the very end as the last few columns. This is much easier than trying to combine the metadata and features table manually.
The code:

library(tidyverse)

library(MicrobeR)

library(qiime2R)

data<-

read_csv(“DKP_lv5_Ixodes_barplot_allD_allK.csv”) %>%

select(-BarcodeSequence, -LinkerPrimerSequence, -Species, -Sample_number, -Description) %>% #remove columns which are not helpful

select(SampleID=index, Sex, Region, everything()) #reorder and rename

data[,4:ncol(data)]<-t(apply(data[,4:ncol(data)], 1, function(x) 100*(x/sum(x)))) # convert to percents maintaining metadata in first 3 columns

TopTaxa<-

data.frame(MeanAbundance=colMeans(data[,4:ncol(data)])) %>% #can figure out the 10 most abundant families to plot, and count everything as other

rownames_to_column(“Family”) %>%

top_n(10, MeanAbundance) %>%

pull(Family)

data %>%

gather(-SampleID, -Sex, -Region, key=Taxon, value=Abundance) %>%

mutate(Family=if_else(Taxon %in% TopTaxa, Taxon, “Other”)) %>%

mutate(Family=factor(Family, levels=rev(c(TopTaxa, “Other”)))) %>%

group_by(SampleID, Sex, Region, Family) %>%

summarize(Abundance=sum(Abundance)) %>% #collapse all other taxa to a single category

ggplot(aes(x=SampleID, y=Abundance, fill=Family)) +

geom_bar(stat=“identity”) +

facet_wrap(Region~Sex, scales=“free_x”) + # creates a separate panel for the combinations of sex and region

theme_bw() + # a good built in theme

coord_cartesian(expand=F) + #remove space around the bars

theme(axis.text.x=element_text(angle=45, hjust=1)) + #rotate x axis text

theme(legend.text = element_text(size=5)) + # family names to fit

scale_fill_manual(

values=rev(c(

  "blue4", 

  "olivedrab", 

  "firebrick", 

  "gold", 

  "darkorchid", 

  "steelblue2", 

  "chartreuse1", 

  "aquamarine", 

  "yellow3", 

  "coral", 

  "grey"))

) #these are the default microber colors but you can change as you see fit or use an automatic color scheme like scale_fill_viridis_d()

ggsave(“barplots.pdf”, width=10, height=7.5)

Looks very nice, thank you.

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