Chapter 3 Convert inputdata into phyloseq object
r Biocpkg(“phyloseq”) (McMurdie and Holmes 2013) is the most popular Biocondcutor package used by the microbiome research field, and phyloseq-class objects are a great data-standard for microbiota data in R. Therefore, the core functions in XMAS take phyloseq-class object as input. In the phyloseq object, information on OTU abundances, taxonomy of OTUs, the phylogenetic tree and metadata is stored.
This tutorial will introduce you the basic steps to convert results from the in-house pipeline into phyloseq-class object. More importantly on how to look at your data and filter appropriately. We will use the inputs from /home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/.
Outline of this Chapter:
3.2 Importing Data
3.2.1 Result from dada2
dada2 results from standardized_analytics_workflow_R_function:
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/16S/process/xdada2/dada2_res.rds
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/16S/process/fasta2tree/tree.nwk
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/16S/metadata.txt
dada2_res <- readRDS(
system.file(
"extdata", "dada2_res.rds",
package = "XMAS2"
)
)
sam_tab <- read.table(
system.file(
"extdata", "dada2_metadata.tsv",
package = "XMAS2"
),
sep = "\t",
header = TRUE,
stringsAsFactors = FALSE
)
tree <- phyloseq::read_tree(
system.file(
"extdata", "tree.nwk",
package = "XMAS2"
)
)3.2.1.1 taxa table
We use import_dada2_taxa to convert dada2_res$tax_tab into our own taxa table
## Kingdom
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "k__Bacteria"
## Phylum
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "p__Firmicutes"
## Class
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "c__Clostridia"
## Order
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "o__Clostridiales"
## Family
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "f__Lachnospiraceae"
## Genus
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "g__Blautia"
## Species
## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG "s__Blautia_unclassified"3.2.1.2 otu table
otu_tab <- dada2_res$seq_tab
# Shouldn't use the Total Number as SampleID (wrong: 123456; right: X123456)
rownames(otu_tab) <- paste0("S", rownames(otu_tab))
head(otu_tab[, 1, F])## TAATACGTAGGGGGCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGGAGCAAACAGG
## S6030 1253
## S6032 14677
## S6033 2929
## S6035 0
## S6036 3548
## S6037 84843.2.1.3 metadata table
sam_tab <- sam_tab %>%
tibble::column_to_rownames("seqID")
# Shouldn't use the Total Number as SampleID (wrong: 123456; right: X123456)
rownames(sam_tab) <- paste0("S", rownames(sam_tab))
head(sam_tab)## Group
## S6065 AA
## S6049 AA
## S6043 AA
## S6037 AA
## S6059 AA
## S6060 AA3.2.1.4 phyloseq object
dada2_ps <- get_dada2_phyloseq(
seq_tab = otu_tab,
tax_tab = tax_tab,
sam_tab = sam_tab,
phy_tree = tree)
dada2_ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 896 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 896 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 896 tips and 893 internal nodes ]
## refseq() DNAStringSet: [ 896 reference sequences ]we obtain the phyloseq-class object and then use it to perform data analysis.
- otu_table
## S6030 S6032 S6033 S6035 S6036 S6037 S6040 S6043 S6045 S6046 S6048 S6049 S6050 S6054 S6055 S6058 S6059 S6060 S6061 S6063 S6065 S6066
## ASV_1 1253 14677 2929 0 3548 8484 11727 5218 4942 7347 3503 3803 14593 4275 2812 0 6847 17893 2956 3328 6302 8414
## ASV_2 2810 1558 1839 11885 0 5632 631 7326 1809 4781 3476 108 3100 39 3879 6389 298 4995 196 45061 542 357
## ASV_3 7107 2915 2200 0 1232 19675 0 1617 839 9362 4815 0 0 1121 13472 0 1421 0 0 0 9694 4387
## ASV_4 0 2280 94 13775 0 4456 937 746 3730 273 85 64 562 448 18 611 96 5976 0 837 0 706
## ASV_5 6983 15963 1172 256 12140 874 1722 120 210 0 111 802 994 1971 18 517 808 443 48 109 7 122
## ASV_6 489 0 199 0 836 0 8565 0 0 1257 0 5998 0 3093 2091 0 10572 0 8495 0 2703 0
## S6068 S8005
## ASV_1 0 0
## ASV_2 10870 7325
## ASV_3 0 14797
## ASV_4 17977 0
## ASV_5 546 2516
## ASV_6 0 974- tax_table
## Kingdom Phylum Class Order Family Genus
## ASV_1 k__Bacteria p__Firmicutes c__Clostridia o__Clostridiales f__Lachnospiraceae g__Blautia
## ASV_2 k__Bacteria p__Actinobacteria c__Actinobacteria o__Bifidobacteriales f__Bifidobacteriaceae g__Bifidobacterium
## ASV_3 k__Bacteria p__Actinobacteria c__Actinobacteria o__Bifidobacteriales f__Bifidobacteriaceae g__Bifidobacterium
## ASV_4 k__Bacteria p__Firmicutes c__Clostridia o__Clostridiales f__Lachnospiraceae g__Lachnospiraceae_unclassified
## ASV_5 k__Bacteria p__Firmicutes c__Bacilli o__Lactobacillales f__Streptococcaceae g__Streptococcus
## ASV_6 k__Bacteria p__Firmicutes c__Clostridia o__Clostridiales f__Ruminococcaceae g__Faecalibacterium
## Species
## ASV_1 s__Blautia_unclassified
## ASV_2 s__Bifidobacterium_unclassified
## ASV_3 s__Bifidobacterium_unclassified
## ASV_4 s__Lachnospiraceae_unclassified
## ASV_5 s__Streptococcus_unclassified
## ASV_6 s__Faecalibacterium_unclassified- sample_table
## Group
## S6030 BB
## S6032 BB
## S6033 BB
## S6035 AA
## S6036 BB
## S6037 AA3.2.2 Result from Metaphlan
The result of the in-house Metaphlan2/3 pipeline:
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/MGS/metaphlan2_merged.tsv
/home/xuxiaomin/project/standardized_analytics_workflow_R_function/demo_data/MGS/metadata.txt
metaphlan2_res <- read.table(
system.file(
"extdata", "metaphlan2_merged.tsv",
package = "XMAS2"
),
header = TRUE,
stringsAsFactors = FALSE
)
metaphlan2_sam <- read.table(
system.file(
"extdata", "metaphlan2_metadata.tsv",
package = "XMAS2"
),
sep = "\t",
header = TRUE,
stringsAsFactors = FALSE
)3.2.2.1 taxa table
metaphlan2_res_list <- import_metaphlan_taxa(
object = metaphlan2_res,
taxa_level = "Species",
trim = TRUE)
tax_tab <- metaphlan2_res_list$tax_tab
head(tax_tab)## Kingdom Phylum Class Order Family Genus
## s__Actinomyces_graevenitzii k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_johnsonii k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_massiliensis k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_odontolyticus k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_oris k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## s__Actinomyces_viscosus k__Bacteria p__Actinobacteria c__Actinobacteria o__Actinomycetales f__Actinomycetaceae g__Actinomyces
## Species
## s__Actinomyces_graevenitzii s__Actinomyces_graevenitzii
## s__Actinomyces_johnsonii s__Actinomyces_johnsonii
## s__Actinomyces_massiliensis s__Actinomyces_massiliensis
## s__Actinomyces_odontolyticus s__Actinomyces_odontolyticus
## s__Actinomyces_oris s__Actinomyces_oris
## s__Actinomyces_viscosus s__Actinomyces_viscosus3.2.2.2 otu table
## s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17
## s__Actinomyces_graevenitzii 3.58e-05 0 0.0e+00 0 0 0 7.20e-06 0.0e+00 0.0000000 0 1.40e-05 0e+00 0.00e+00 0.0e+00 0 0 0
## s__Actinomyces_johnsonii 0.00e+00 0 0.0e+00 0 0 0 0.00e+00 0.0e+00 0.0000000 0 0.00e+00 0e+00 1.15e-05 0.0e+00 0 0 0
## s__Actinomyces_massiliensis 0.00e+00 0 0.0e+00 0 0 0 2.95e-05 0.0e+00 0.0000000 0 0.00e+00 0e+00 0.00e+00 0.0e+00 0 0 0
## s__Actinomyces_odontolyticus 7.70e-05 0 1.7e-06 0 0 0 2.47e-05 5.8e-06 0.0001175 0 6.16e-05 0e+00 1.66e-05 3.9e-06 0 0 0
## s__Actinomyces_oris 0.00e+00 0 0.0e+00 0 0 0 2.81e-05 0.0e+00 0.0000135 0 0.00e+00 0e+00 0.00e+00 0.0e+00 0 0 0
## s__Actinomyces_viscosus 4.60e-06 0 0.0e+00 0 0 0 0.00e+00 0.0e+00 0.0000039 0 6.20e-06 7e-06 1.10e-06 0.0e+00 0 0 0
## s18 s19 s20 s21 s22 refE
## s__Actinomyces_graevenitzii 6.1e-06 1.60e-06 0 0 0.00e+00 0
## s__Actinomyces_johnsonii 0.0e+00 0.00e+00 0 0 0.00e+00 0
## s__Actinomyces_massiliensis 0.0e+00 0.00e+00 0 0 2.52e-05 0
## s__Actinomyces_odontolyticus 0.0e+00 4.19e-05 0 0 0.00e+00 0
## s__Actinomyces_oris 0.0e+00 0.00e+00 0 0 7.40e-06 0
## s__Actinomyces_viscosus 0.0e+00 0.00e+00 0 0 7.28e-05 03.2.2.3 metadata table
## Group phynotype
## s1 BB 0.00
## s2 AA 2.50
## s3 BB 0.00
## s4 AA 1.25
## s5 AA 30.00
## s6 AA 15.003.2.2.4 phyloseq object
metaphlan2_ps <- get_metaphlan_phyloseq(
otu_tab = otu_tab,
sam_tab = sam_tab,
tax_tab = tax_tab)
metaphlan2_ps## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 322 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 322 taxa by 7 taxonomic ranks ]3.3 Systematic Information
## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.3 (2022-03-10)
## os macOS Monterey 12.2.1
## system x86_64, darwin17.0
## ui RStudio
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Asia/Shanghai
## date 2023-11-30
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
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## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────References
McMurdie, Paul J, and Susan Holmes. 2013. “Phyloseq: An r Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” PloS One 8 (4): e61217.