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:
5.2 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 S6058 S6059
## g__Acetanaerobacterium 0 0 0 0 0.000000 0.000000 0 0 0.000000 0 0 0.00000 0 0.30103 0 0 0
## g__Acidaminococcus 0 0 0 0 2.481443 3.066326 0 0 2.326336 0 0 0.00000 0 0.00000 0 0 0
## g__Acinetobacter 0 0 0 0 0.000000 0.000000 0 0 0.000000 0 0 0.69897 0 0.00000 0 0 0
## S6060 S6061 S6063 S6065 S6066 S6068
## g__Acetanaerobacterium 0.00000 0 0.000000 0 0 0
## g__Acidaminococcus 0.60206 0 1.812913 0 0 0
## g__Acinetobacter 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 s19 s20 s21
## 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 0 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 0 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 0 0 0
## s22
## s__Abiotrophia_defectiva 0.000000
## s__Acidaminococcus_fermentans -2.616418
## s__Acidaminococcus_intestini -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.
The corresponding XMAS2 argument is normMethod with the available options: “rarefy”, “TSS”, “TMM”, “RLE”, “CSS”, “CLR” and “CPM”.
| Method | Approach | Comments |
|---|---|---|
| rarefy (Rarefying) | Subsampling from the data to obtain samples with equal library size (also called rarefaction level) . | Aims to avoid compositional effects. |
| TSS (Total Sum Scaling) | Traditional approach for building fractions. Counts are divided by the total sum of counts in the corresponding sample. | Strongly influenced by highly abundant taxa. |
| 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. | Aims to avoid sequencing depth effects. |
| 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. | Similar to TMM, this normalization method is based on the hypothesis that the most genes are not DE. |
| CSS (Cumulative Sum Scaling) | Within each sample, the counts are summed up to a predefined quantile. The counts are then divided by this sum. | Aims at avoiding the influence of highly abundant taxa. |
| CLR (Centered log-ratio transformation) | For a composition \[x = (x_{1} , ..., x_{p})\] the clr transformation is defined as \[clr = log(\frac{x_{1}}{g(x)}, ..., \frac{x_{p}}{g(x)})\], where \[g(x)=(\prod_{p}^{k=1} x_{k})^{\frac{1}{p}}\] is the geometric mean. | Aims to avoid compositional effects. |
| CPM (Counts Per Million) | pre-sample normalization of the sum of the values to 1e+06. | Aims to avoid sequencing depth effects. |
- 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).
## 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 ]- TSS: total sum scaling, also referred to as “relative abundance”, the abundances were normalized by dividing the corresponding sample library size
## 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.
## 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.
## 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.
## 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.
## 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.
## 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: [ 110 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 110 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
## 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
## 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
## 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
5.8 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
## 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
## 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
## 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
## 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 ]## [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 ]## [1] "g__Subdoligranulum" "g__Sutterella" "g__T4likevirus" "g__Turicibacter" "g__Veillonella" "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
## ─ 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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## pkgbuild 1.4.2 2023-06-26 [2] CRAN (R 4.1.3)
## pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.1.0)
## pkgload 1.3.2.1 2023-07-08 [2] CRAN (R 4.1.3)
## plyr 1.8.8 2022-11-11 [2] CRAN (R 4.1.2)
## png 0.1-8 2022-11-29 [2] CRAN (R 4.1.2)
## prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.1.0)
## processx 3.8.2 2023-06-30 [2] CRAN (R 4.1.3)
## profvis 0.3.8 2023-05-02 [2] CRAN (R 4.1.2)
## promises 1.2.0.1 2021-02-11 [2] CRAN (R 4.1.0)
## ps 1.7.5 2023-04-18 [2] CRAN (R 4.1.2)
## purrr 1.0.1 2023-01-10 [2] CRAN (R 4.1.2)
## R6 2.5.1 2021-08-19 [2] CRAN (R 4.1.0)
## RColorBrewer 1.1-3 2022-04-03 [2] CRAN (R 4.1.2)
## Rcpp 1.0.11 2023-07-06 [1] CRAN (R 4.1.3)
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## reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.1.0)
## rhdf5 2.38.1 2022-03-10 [2] Bioconductor
## rhdf5filters 1.6.0 2021-10-26 [2] Bioconductor
## Rhdf5lib 1.16.0 2021-10-26 [2] Bioconductor
## rlang 1.1.1 2023-04-28 [1] CRAN (R 4.1.2)
## rmarkdown 2.23 2023-07-01 [2] CRAN (R 4.1.3)
## rpart 4.1.19 2022-10-21 [2] CRAN (R 4.1.2)
## RSQLite 2.3.1 2023-04-03 [2] CRAN (R 4.1.2)
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## rvest 1.0.3 2022-08-19 [2] CRAN (R 4.1.2)
## S4Vectors 0.32.4 2022-03-29 [2] Bioconductor
## sandwich 3.0-2 2022-06-15 [2] CRAN (R 4.1.2)
## sass 0.4.6 2023-05-03 [2] CRAN (R 4.1.2)
## scales 1.2.1 2022-08-20 [2] CRAN (R 4.1.2)
## scatterplot3d 0.3-44 2023-05-05 [2] CRAN (R 4.1.2)
## sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.1.0)
## shape 1.4.6 2021-05-19 [2] CRAN (R 4.1.0)
## shiny 1.7.4.1 2023-07-06 [2] CRAN (R 4.1.3)
## stringi 1.7.12 2023-01-11 [2] CRAN (R 4.1.2)
## stringr 1.5.0 2022-12-02 [2] CRAN (R 4.1.2)
## SummarizedExperiment 1.24.0 2021-10-26 [2] Bioconductor
## survival 3.5-5 2023-03-12 [2] CRAN (R 4.1.2)
## svglite 2.1.1 2023-01-10 [2] CRAN (R 4.1.2)
## systemfonts 1.0.4 2022-02-11 [2] CRAN (R 4.1.2)
## TH.data 1.1-2 2023-04-17 [2] CRAN (R 4.1.2)
## tibble * 3.2.1 2023-03-20 [2] CRAN (R 4.1.2)
## tidyr 1.3.0 2023-01-24 [2] CRAN (R 4.1.2)
## tidyselect 1.2.0 2022-10-10 [2] CRAN (R 4.1.2)
## urlchecker 1.0.1 2021-11-30 [2] CRAN (R 4.1.0)
## usethis 2.2.2 2023-07-06 [2] CRAN (R 4.1.3)
## utf8 1.2.3 2023-01-31 [2] CRAN (R 4.1.2)
## vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.1.3)
## vegan 2.6-4 2022-10-11 [2] CRAN (R 4.1.2)
## viridisLite 0.4.2 2023-05-02 [2] CRAN (R 4.1.2)
## webshot 0.5.5 2023-06-26 [2] CRAN (R 4.1.3)
## withr 2.5.0 2022-03-03 [2] CRAN (R 4.1.2)
## Wrench 1.12.0 2021-10-26 [2] Bioconductor
## xfun 0.40 2023-08-09 [1] CRAN (R 4.1.3)
## XMAS2 * 2.2.0 2023-11-30 [1] local
## XML 3.99-0.14 2023-03-19 [2] CRAN (R 4.1.2)
## xml2 1.3.5 2023-07-06 [2] CRAN (R 4.1.3)
## xtable 1.8-4 2019-04-21 [2] CRAN (R 4.1.0)
## XVector 0.34.0 2021-10-26 [2] Bioconductor
## yaml 2.3.7 2023-01-23 [2] CRAN (R 4.1.2)
## zlibbioc 1.40.0 2021-10-26 [2] Bioconductor
## zoo 1.8-12 2023-04-13 [2] CRAN (R 4.1.2)
##
## [1] /Users/zouhua/Library/R/x86_64/4.1/library
## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
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