ycl6
commented
2 years ago Hi @lauramason326
Below I am using the demo dataset GlobalPatterns to first construct a data.frame similar to your data structure. You can adapt the rest of the code to your real data.
library(phyloseq)
library(tidyverse)
library(stringr) # str_pad
data(GlobalPatterns)
df <- GlobalPatterns %>% psmelt()
# Think of OTU as the sequence info in your data
df <- df[,c("Sample","OTU","Abundance","Kingdom","Phylum","Class","Order","Family","Genus","Species")]> head(df, 10)
Sample OTU Abundance Kingdom Phylum Class Order Family Genus Species
406582 AQC4cm 549656 1177685 Bacteria Cyanobacteria Chloroplast Stramenopiles <NA> <NA> <NA>
241435 LMEpi24M 279599 914209 Bacteria Cyanobacteria Nostocophycideae Nostocales Nostocaceae Dolichospermum <NA>
406580 AQC7cm 549656 711043 Bacteria Cyanobacteria Chloroplast Stramenopiles <NA> <NA> <NA>
406574 AQC1cm 549656 554198 Bacteria Cyanobacteria Chloroplast Stramenopiles <NA> <NA> <NA>
329873 M31Tong 360229 540850 Bacteria Proteobacteria Betaproteobacteria Neisseriales Neisseriaceae Neisseria <NA>
300794 M11Fcsw 331820 452219 Bacteria Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides <NA>
494797 M31Tong 94166 396201 Bacteria Proteobacteria Gammaproteobacteria Pasteurellales Pasteurellaceae Haemophilus Haemophilusparainfluenzae
300772 M31Fcsw 331820 354695 Bacteria Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides <NA>
298689 SLEpi20M 329744 323914 Bacteria Actinobacteria Actinobacteria Actinomycetales ACK-M1 <NA> <NA>
114279 TS29 189047 251215 Bacteria Firmicutes Clostridia Clostridiales Ruminococcaceae <NA> <NA>Below I convert Sample and OTU to factor, rename OTU ID to ASV_xxxxx and sort the data.frame.
df$Sample <- as.factor(df$Sample)
df$OTU <- as.factor(df$OTU)
levels(df$OTU) <- paste0("ASV_", str_pad(1:nlevels(df$OTU), nchar(nlevels(df$OTU)), pad = "0"))
df <- df[order(df$Sample, df$OTU),]> head(df, 10)
Sample OTU Abundance Kingdom Phylum Class Order Family Genus Species
11 AQC1cm ASV_00001 0 Bacteria Actinobacteria Actinobacteria Actinomycetales Microbacteriaceae Cryocola <NA>
36 AQC1cm ASV_00002 0 Bacteria Proteobacteria Alphaproteobacteria Sphingomonadales Sphingomonadaceae <NA> <NA>
65 AQC1cm ASV_00003 0 Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobulbaceae <NA> <NA>
88 AQC1cm ASV_00004 1 Bacteria Proteobacteria Gammaproteobacteria Thiotrichales <NA> <NA> <NA>
128 AQC1cm ASV_00005 0 Bacteria Proteobacteria Gammaproteobacteria Thiotrichales Thiotrichaceae Thiothrix <NA>
144 AQC1cm ASV_00006 0 Bacteria Firmicutes Clostridia Clostridiales Lachnospiraceae Ruminococcus <NA>
165 AQC1cm ASV_00007 1 Bacteria Proteobacteria Betaproteobacteria Rhodocyclales Rhodocyclaceae Methyloversatilis <NA>
198 AQC1cm ASV_00008 1 Bacteria Bacteroidetes Flavobacteria <NA> <NA> <NA> <NA>
230 AQC1cm ASV_00009 1 Bacteria Proteobacteria Betaproteobacteria Burkholderiales Comamonadaceae <NA> <NA>
246 AQC1cm ASV_00010 11 Bacteria Proteobacteria Betaproteobacteria Burkholderiales Burkholderiaceae Cupriavidus <NA>They we build the required data for phyloseq. In sam, you can add other sample variables you have to the data.frame at this point.
# sample_data
sam <- data.frame(SampleID = levels(df$Sample), row.names = levels(df$Sample))
# tax_table
tax <- unique(df[,c("OTU","Kingdom","Phylum","Class","Order","Family","Genus","Species")])
rownames(tax) <- tax$OTU
tax <- as.matrix(tax[,-1]) # remove OTU column
# otu_table
otu <- df %>% select(OTU, Sample, Abundance) %>% spread(Sample, Abundance)
rownames(otu) <- otu$OTU
otu <- otu[,-1] # remove OTU column
# Check rownames and colnames matched
all(rownames(otu) == rownames(tax_table(tax))) # TRUE
all(colnames(otu) == rownames(sample_data(sam))) # TRUE
ps <- phyloseq(otu_table(otu, taxa_are_rows = TRUE), tax_table(tax), sample_data(sam))> head(sam)
SampleID
AQC1cm AQC1cm
AQC4cm AQC4cm
AQC7cm AQC7cm
CC1 CC1
CL3 CL3
Even1 Even1
> head(tax)
Kingdom Phylum Class Order Family Genus Species
ASV_00001 "Bacteria" "Actinobacteria" "Actinobacteria" "Actinomycetales" "Microbacteriaceae" "Cryocola" NA
ASV_00002 "Bacteria" "Proteobacteria" "Alphaproteobacteria" "Sphingomonadales" "Sphingomonadaceae" NA NA
ASV_00003 "Bacteria" "Proteobacteria" "Deltaproteobacteria" "Desulfobacterales" "Desulfobulbaceae" NA NA
ASV_00004 "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Thiotrichales" NA NA NA
ASV_00005 "Bacteria" "Proteobacteria" "Gammaproteobacteria" "Thiotrichales" "Thiotrichaceae" "Thiothrix" NA
ASV_00006 "Bacteria" "Firmicutes" "Clostridia" "Clostridiales" "Lachnospiraceae" "Ruminococcus" NA
> head(otu)
AQC1cm AQC4cm AQC7cm CC1 CL3 Even1 Even2 Even3 F21Plmr LMEpi24M M11Fcsw M11Plmr M11Tong M31Fcsw M31Plmr M31Tong NP2 NP3
ASV_00001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ASV_00002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ASV_00003 0 3 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 4
ASV_00004 1 7 1 136 5 0 1 0 0 0 0 0 0 0 0 0 0 0
ASV_00005 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0
ASV_00006 0 0 0 0 0 0 2 1 0 0 0 0 0 58 0 0 0 0
NP5 SLEpi20M SV1 TRRsed1 TRRsed2 TRRsed3 TS28 TS29
ASV_00001 0 0 7 0 0 0 0 0
ASV_00002 0 0 7 0 0 0 0 0
ASV_00003 0 0 0 6 71 44 0 0
ASV_00004 0 0 0 2 2 0 0 0
ASV_00005 0 0 0 0 0 0 0 0
ASV_00006 0 0 0 0 0 0 1 0
> ps
phyloseq-class experiment-level object
otu_table() OTU Table: [ 19216 taxa and 26 samples ]
sample_data() Sample Data: [ 26 samples by 1 sample variables ]
tax_table() Taxonomy Table: [ 19216 taxa by 7 taxonomic ranks ]
Hello, I am trying to import a file into phyloseq that does not look like the files in the tutorials. The samples are repeated mulitple times, as are the sequences. I would like to treat the sequences as OTU_IDs, and somehow manipulate the file to get a "regular" OTU table with samples across the top, OTU_IDs in the first column, and counts within. Can you help me?
Thanks! Laura