Chapter 2 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/.
Loading packages
library(XMAS2)
library(dplyr)
library(tibble)
library(phyloseq)2.1 DADA2
dada2 results from standardized_analytics_workflow_R_function
2.1.1 Importing results from dada2 pipeline
/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"
)
)2.1.2 taxa table
We use import_dada2_taxa to convert dada2_res$tax_tab into our own taxa table
tax_tab <- import_dada2_taxa(dada2_taxa = dada2_res$tax_tab)
head(tax_tab, 1)## 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"2.1.3 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 84842.1.4 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 AA2.1.5 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.
Here, the phyloseq object comprises five components (OTU Table, Sample Data, Taxonomy Table, Phylogenetic Tree and DNAStringSet).
- otu_table
dada2_ps@otu_table %>% data.frame() %>% head()## S6030 S6032 S6033 S6035 S6036 S6037 S6040 S6043 S6045 S6046 S6048 S6049 S6050 S6054 S6055 S6058 S6059 S6060 S6061 S6063
## ASV_1 1253 14677 2929 0 3548 8484 11727 5218 4942 7347 3503 3803 14593 4275 2812 0 6847 17893 2956 3328
## ASV_2 2810 1558 1839 11885 0 5632 631 7326 1809 4781 3476 108 3100 39 3879 6389 298 4995 196 45061
## ASV_3 7107 2915 2200 0 1232 19675 0 1617 839 9362 4815 0 0 1121 13472 0 1421 0 0 0
## ASV_4 0 2280 94 13775 0 4456 937 746 3730 273 85 64 562 448 18 611 96 5976 0 837
## ASV_5 6983 15963 1172 256 12140 874 1722 120 210 0 111 802 994 1971 18 517 808 443 48 109
## ASV_6 489 0 199 0 836 0 8565 0 0 1257 0 5998 0 3093 2091 0 10572 0 8495 0
## S6065 S6066 S6068 S8005
## ASV_1 6302 8414 0 0
## ASV_2 542 357 10870 7325
## ASV_3 9694 4387 0 14797
## ASV_4 0 706 17977 0
## ASV_5 7 122 546 2516
## ASV_6 2703 0 0 974- tax_table
dada2_ps@tax_table %>% data.frame() %>% head()## Kingdom Phylum Class Order Family
## ASV_1 k__Bacteria p__Firmicutes c__Clostridia o__Clostridiales f__Lachnospiraceae
## ASV_2 k__Bacteria p__Actinobacteria c__Actinobacteria o__Bifidobacteriales f__Bifidobacteriaceae
## ASV_3 k__Bacteria p__Actinobacteria c__Actinobacteria o__Bifidobacteriales f__Bifidobacteriaceae
## ASV_4 k__Bacteria p__Firmicutes c__Clostridia o__Clostridiales f__Lachnospiraceae
## ASV_5 k__Bacteria p__Firmicutes c__Bacilli o__Lactobacillales f__Streptococcaceae
## ASV_6 k__Bacteria p__Firmicutes c__Clostridia o__Clostridiales f__Ruminococcaceae
## Genus Species
## ASV_1 g__Blautia s__Blautia_unclassified
## ASV_2 g__Bifidobacterium s__Bifidobacterium_unclassified
## ASV_3 g__Bifidobacterium s__Bifidobacterium_unclassified
## ASV_4 g__Lachnospiraceae_unclassified s__Lachnospiraceae_unclassified
## ASV_5 g__Streptococcus s__Streptococcus_unclassified
## ASV_6 g__Faecalibacterium s__Faecalibacterium_unclassified- sample_table
dada2_ps@sam_data %>% data.frame() %>% head()## Group
## S6030 BB
## S6032 BB
## S6033 BB
## S6035 AA
## S6036 BB
## S6037 AA2.2 Summarize phyloseq-class object
summarize_phyloseq(ps = dada2_ps)
## Compositional = NO2
## 1] Min. number of reads = 511812] Max. number of reads = 936223] Total number of reads = 15025374] Average number of reads = 62605.70833333335] Median number of reads = 619157] Sparsity = 0.8653738839285716] Any OTU sum to 1 or less? NO8] Number of singletons = 09] Percent of OTUs that are singletons
## (i.e. exactly one read detected across all samples)010] Number of sample variables are: 1Group2
## [[1]]
## [1] "1] Min. number of reads = 51181"
##
## [[2]]
## [1] "2] Max. number of reads = 93622"
##
## [[3]]
## [1] "3] Total number of reads = 1502537"
##
## [[4]]
## [1] "4] Average number of reads = 62605.7083333333"
##
## [[5]]
## [1] "5] Median number of reads = 61915"
##
## [[6]]
## [1] "7] Sparsity = 0.865373883928571"
##
## [[7]]
## [1] "6] Any OTU sum to 1 or less? NO"
##
## [[8]]
## [1] "8] Number of singletons = 0"
##
## [[9]]
## [1] "9] Percent of OTUs that are singletons\n (i.e. exactly one read detected across all samples)0"
##
## [[10]]
## [1] "10] Number of sample variables are: 1"
##
## [[11]]
## [1] "Group"The minus account of the OTU counts is 51181 in the phyloseq object, and we can use it as the threshold to rarefy.
Notice the Sparsity (0.865), indicating the data has many zeros and pay attention to the downstream data analysis. A common property of amplicon based microbiota data generated by sequencing.
2.3 Systematic Information
devtools::session_info()## ─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## 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 2022-08-09
## rstudio 2022.07.1+554 Spotted Wakerobin (desktop)
## pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
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##
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────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.