Chapter 5 Pre-processing
Performing the pre-processing steps on the phyloseq-class object. Data Processing is a critical procedure in data analysis. In this chapter, we would use the following steps to transform, normalize, and trim microbiota data.
Outline of this Chapter:
5.1 Loading Packages
library(XMAS2)
library(dplyr)
library(tibble)
library(phyloseq)
library(ggplot2)
library(ggpubr)
library(conflicted)
conflicted::conflict_prefer("normalize", "XMAS2")5.2 Importing Data
data("dada2_ps")
dada2_ps_remove_BRS <- get_GroupPhyloseq(
ps = dada2_ps,
group = "Group",
group_names = "QC",
discard = TRUE)
dada2_ps_remove_BRS## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 896 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 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 ]metagenomic sequencing (metaphlan2/3)
data("metaphlan2_ps")
metaphlan2_ps_remove_BRS <- get_GroupPhyloseq(
ps = metaphlan2_ps,
group = "Group",
group_names = "QC",
discard = TRUE)
metaphlan2_ps_remove_BRS## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 326 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 326 taxa by 7 taxonomic ranks ]5.3 Transformation
Transforming the taxa abundances in otu_table by sample, which means the counts of each sample will be transformed individually. The options include:
“identity”, return the original data without any transformation;
“log10”, the transformation is
log10(object), and if the data contains zeros the transformation islog10(1 + object);“log10p”, the transformation is
log10(1 + object).
dada2_ps_genus <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Genus")
dada2_ps_transform <- transform_abundances(object = dada2_ps_genus,
transform = "log10")
head(dada2_ps_transform@otu_table@.Data, 3)## S6030 S6032 S6033 S6035 S6036 S6037 S6040 S6043 S6045 S6046 S6048 S6049 S6050 S6054 S6055
## g__Acetanaerobacterium 0 0 0 0 0.000000 0.000000 0 0 0.000000 0 0 0.00000 0 0.30103 0
## g__Acidaminococcus 0 0 0 0 2.481443 3.066326 0 0 2.326336 0 0 0.00000 0 0.00000 0
## g__Acinetobacter 0 0 0 0 0.000000 0.000000 0 0 0.000000 0 0 0.69897 0 0.00000 0
## S6058 S6059 S6060 S6061 S6063 S6065 S6066 S6068
## g__Acetanaerobacterium 0 0 0.00000 0 0.000000 0 0 0
## g__Acidaminococcus 0 0 0.60206 0 1.812913 0 0 0
## g__Acinetobacter 0 0 0.00000 0 0.000000 0 0 0transforming metagenomic sequencing
metaphlan2_ps_species <- summarize_taxa(ps = metaphlan2_ps_remove_BRS,
taxa_level = "Species")
metaphlan2_ps_transform_mgs <- transform_abundances(object = metaphlan2_ps_species,
transform = "log10")
head(metaphlan2_ps_transform_mgs@otu_table@.Data, 3)## s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18
## s__Abiotrophia_defectiva -4.610834 0 0 0 0 0 0.000000 0.000000 0.000000 0 0 0 0 0 0 0.000000 0 0
## s__Acidaminococcus_fermentans 0.000000 0 0 0 0 0 0.000000 -2.957621 -2.180555 0 0 0 0 0 0 -2.326583 0 0
## s__Acidaminococcus_intestini -5.376751 0 0 0 0 0 -3.222573 -2.997748 0.000000 0 0 0 0 0 0 0.000000 0 0
## s19 s20 s21 s22
## s__Abiotrophia_defectiva 0 0 0 0.000000
## s__Acidaminococcus_fermentans 0 0 0 -2.616418
## s__Acidaminococcus_intestini 0 0 0 -2.8319455.4 Normalization
Normalizing the OTU_table in phyloseq-class object. It is critical to normalize the feature table to eliminate any bias due to differences in the sampling sequencing depth. This function implements 7 widely-used normalization methods for microbial compositional data.
- rarefy: random subsampling counts to the smallest library size in the data set. Caution: the default library size is 25000 according to our own results(Rarefaction Curves).
XMAS2::normalize(object = dada2_ps_genus,
method = "rarefy")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]# norm_rarefy(dada2_ps_genus, size = 50000)- TSS: total sum scaling, also referred to as “relative abundance”, the abundances were normalized by dividing the corresponding sample library size
XMAS2::normalize(object = dada2_ps_genus,
method = "TSS")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]- TMM: trimmed mean of m-values. First, a sample is chosen as reference. The scaling factor is then derived using a weighted trimmed mean over the differences of the log-transformed gene-count fold-change between the sample and the reference.
XMAS2::normalize(object = dada2_ps_genus,
method = "TMM")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]- RLE: relative log expression, RLE uses a pseudo-reference calculated using the geometric mean of the gene-specific abundances over all samples. The scaling factors are then calculated as the median of the gene counts ratios between the samples and the reference.
XMAS2::normalize(object = dada2_ps_genus,
method = "RLE")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]- CSS: cumulative sum scaling, calculates scaling factors as the cumulative sum of gene abundances up to a data-derived threshold. While standard relative abundance (fraction/percentage) normalization re-scales all samples to the same total sum (100%), CSS keeps a variation in total counts between samples. CSS re-scales the samples based on a subset (quartile) of lower abundant taxa (relatively constant and independent), thereby excluding the impact of (study dominating) high abundant taxa.
XMAS2::normalize(object = dada2_ps_genus,
method = "CSS")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]- CLR: centered log-ratio normalization.
XMAS2::normalize(object = dada2_ps_genus,
method = "CLR")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]- CPM: pre-sample normalization of the sum of the values to 1e+06.
XMAS2::normalize(object = dada2_ps_genus,
method = "CPM")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]5.5 Filtering
Whether to filter the low relative abundance or unclassified taxa by the threshold.
ps_genus_rb <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Genus",
absolute = FALSE)
ps_genus_rb## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]ps_genus_rb_filter <- run_filter(ps = ps_genus_rb,
cutoff = 1e-04,
unclass = TRUE)
ps_genus_rb_filter## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 160 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 160 taxa by 6 taxonomic ranks ]39 genus’s relative abundance or attributes were below 1e-04 or unclassified and they were removed by the cutoff.
5.6 Trimming
The previous function (run_filter) only focuses on the low relative abundance and unclassified taxa. Microbial data always have so many zeros. Trimming samples or taxa in otu_table by occurrences or prevalence before downstream analysis is also crucial.
- trimming by TaxaID
run_trim(object = dada2_ps_genus,
cutoff = 0.4,
trim = "feature")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 63 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 63 taxa by 6 taxonomic ranks ]Dropping the taxa whose prevalence or occurrences are less than 0.4.
- trimming by SampleID
run_trim(object = dada2_ps_genus,
cutoff = 0.4,
trim = "sample")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 4 samples ]
## sample_data() Sample Data: [ 4 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]Dropping the samples whose prevalence or occurrences are less than 0.4.
- trimming by TaxaID & SampleID
run_trim(object = dada2_ps_genus,
cutoff = 0.4,
trim = "both")## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 63 taxa and 4 samples ]
## sample_data() Sample Data: [ 4 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 63 taxa by 6 taxonomic ranks ]Dropping the taxa and samples whose prevalence are less than 0.4.
filtering metagenomic sequencing
metaphlan2_ps_trim_mgs <- run_trim(object = metaphlan2_ps_remove_BRS,
cutoff = 0.4,
trim = "feature")
metaphlan2_ps_trim_mgs## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 102 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 102 taxa by 7 taxonomic ranks ]5.7 Imputation
The missing values in otu table maybe affect the statistical results, imputing the NAs or Zero values should taken into account.
- limit of detection
min(dada2_ps_genus@otu_table)
## [1] 0
LOD_imputed_ps <- run_impute(object = dada2_ps_genus,
impute = "LOD",
LOD = 2)
min(LOD_imputed_ps@otu_table)
## [1] 25.8 Extracting specific taxa phyloseq-class object
The taxonomic level are Kingdom, Phylum, Class, Order, Family, Genus, Species and choosing the specific taxa to regenerate the phyloseq-class object.
- amplicon sequencing: Phylum
dada2_ps_phylum <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Phylum")
dada2_ps_phylum## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 10 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 10 taxa by 2 taxonomic ranks ]- amplicon sequencing: Order
dada2_ps_order <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Order")
dada2_ps_order## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 24 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 24 taxa by 4 taxonomic ranks ]- amplicon sequencing: Family
dada2_ps_family <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Family")
dada2_ps_family## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 54 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 54 taxa by 5 taxonomic ranks ]- amplicon sequencing: Genus
dada2_ps_genus <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Genus")
dada2_ps_genus## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 198 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 198 taxa by 6 taxonomic ranks ]extracting metagenomic sequencing
metaphlan2_ps_genus <- summarize_taxa(ps = metaphlan2_ps_remove_BRS,
taxa_level = "Genus")
metaphlan2_ps_genus## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 109 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 109 taxa by 6 taxonomic ranks ]metaphlan2_ps_species <- summarize_taxa(ps = metaphlan2_ps_remove_BRS,
taxa_level = "Species")
metaphlan2_ps_species## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 325 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 325 taxa by 7 taxonomic ranks ]5.9 Aggregating low relative abundance or unclassified taxa into others
Taxa with relative abundance less than 0.0001 will be summarized into
Others_LowAbundance;Unclassified taxa will be summarized into
Others_Unclassified.
- amplicon sequencing
# relative abundance
dada2_ps_genus_rb <- summarize_taxa(ps = dada2_ps_remove_BRS,
taxa_level = "Genus",
absolute = FALSE)
dada2_ps_genus_LRA <- summarize_LowAbundance_taxa(ps = ps_genus_rb,
cutoff = 1e-04,
unclass = TRUE)
dada2_ps_genus_LRA ## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 162 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 162 taxa by 6 taxonomic ranks ]tail(phyloseq::taxa_names(dada2_ps_genus_LRA))## [1] "g__Tyzzerella_3" "g__Tyzzerella_4" "g__UBA1819" "g__UC5_1_2E3" "g__Veillonella" "g__Weissella"# absolute abundance
dada2_ps_genus_counts <- summarize_LowAbundance_taxa(ps = dada2_ps_genus,
cutoff = 10)
dada2_ps_genus_counts## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 173 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 173 taxa by 6 taxonomic ranks ]aggregating metagenomic sequencing
metaphlan2_ps_genus_LRA <- summarize_LowAbundance_taxa(ps = metaphlan2_ps_genus,
cutoff = 1e-04,
unclass = TRUE)
metaphlan2_ps_genus_LRA## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 78 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 78 taxa by 6 taxonomic ranks ]tail(phyloseq::taxa_names(metaphlan2_ps_genus_LRA))## [1] "g__Subdoligranulum" "g__Sutterella" "g__T4likevirus" "g__Turicibacter" "g__Veillonella"
## [6] "g__Weissella"5.10 Transform the abundance of taxa whose relative abundance under Limit Of Detection (LOD) into Zeros (only in metaphlan2/3)
metaphlan2_ps_Species_LOD <- aggregate_LOD_taxa(ps = metaphlan2_ps,
taxa_level = "Species",
cutoff = 1e-04)
metaphlan2_ps_Species_LOD## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 192 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 192 taxa by 7 taxonomic ranks ]5.11 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 2022-10-08
## rstudio 2022.07.1+554 Spotted Wakerobin (desktop)
## pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.0)
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## lattice * 0.20-45 2021-09-22 [1] CRAN (R 4.1.2)
## latticeExtra 0.6-29 2019-12-19 [1] CRAN (R 4.1.0)
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## lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.0)
## limma 3.50.1 2022-02-17 [1] Bioconductor
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## LOCOM 1.1 2022-08-05 [1] Github (yijuanhu/LOCOM@c181e0f)
## lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.1.0)
## magrittr * 2.0.2 2022-01-26 [1] CRAN (R 4.1.2)
## MASS 7.3-55 2022-01-13 [1] CRAN (R 4.1.2)
## Matrix 1.4-0 2021-12-08 [1] CRAN (R 4.1.0)
## MatrixGenerics * 1.6.0 2021-10-26 [1] Bioconductor
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## microbiome 1.16.0 2021-10-26 [1] Bioconductor
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## multtest 2.50.0 2021-10-26 [1] Bioconductor
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## permute * 0.9-7 2022-01-27 [1] CRAN (R 4.1.2)
## pheatmap * 1.0.12 2019-01-04 [1] CRAN (R 4.1.0)
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## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0)
## pkgload 1.2.4 2021-11-30 [1] CRAN (R 4.1.0)
## plyr 1.8.6 2020-03-03 [1] CRAN (R 4.1.0)
## png 0.1-7 2013-12-03 [1] CRAN (R 4.1.0)
## ppcor 1.1 2015-12-03 [1] CRAN (R 4.1.0)
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## reticulate 1.24 2022-01-26 [1] CRAN (R 4.1.2)
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## rhdf5 2.38.1 2022-03-10 [1] Bioconductor
## rhdf5filters 1.6.0 2021-10-26 [1] Bioconductor
## Rhdf5lib 1.16.0 2021-10-26 [1] Bioconductor
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## Rtsne 0.15 2018-11-10 [1] CRAN (R 4.1.0)
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## scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.0)
## scatterplot3d 0.3-41 2018-03-14 [1] CRAN (R 4.1.0)
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## sf 1.0-7 2022-03-07 [1] CRAN (R 4.1.2)
## shape 1.4.6 2021-05-19 [1] CRAN (R 4.1.0)
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## sjmisc 2.8.9 2021-12-03 [1] CRAN (R 4.1.0)
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## stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.0)
## SummarizedExperiment * 1.24.0 2021-10-26 [1] Bioconductor
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## tensorA 0.36.2 2020-11-19 [1] CRAN (R 4.1.0)
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## TH.data 1.1-0 2021-09-27 [1] CRAN (R 4.1.0)
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## vegan * 2.5-7 2020-11-28 [1] CRAN (R 4.1.0)
## VennDiagram * 1.7.3 2022-04-12 [1] CRAN (R 4.1.2)
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## viridisLite * 0.4.0 2021-04-13 [1] CRAN (R 4.1.0)
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## Wrench 1.12.0 2021-10-26 [1] Bioconductor
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## XMAS2 * 2.1.8 2022-10-08 [1] local
## XML 3.99-0.9 2022-02-24 [1] CRAN (R 4.1.2)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.1.0)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.1.0)
## XVector 0.34.0 2021-10-26 [1] Bioconductor
## yaml 2.3.5 2022-02-21 [1] CRAN (R 4.1.2)
## zCompositions 1.4.0 2022-01-13 [1] CRAN (R 4.1.2)
## zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
## zoo 1.8-9 2021-03-09 [1] CRAN (R 4.1.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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