Chapter 3 Data Processing
Outline of this Chapter:
3.2 Importing Data
- Converting the output of dada2 into phyloseq object
seq_tab <- readRDS(
system.file("extdata", "dada2_seqtab.rds",
package = "MicrobiomeAnalysis"))
tax_tab <- readRDS(
system.file("extdata", "dada2_taxtab.rds",
package = "MicrobiomeAnalysis"))
sam_tab <- read.table(
system.file("extdata", "dada2_samdata.txt",
package = "MicrobiomeAnalysis"),
sep = "\t", header = TRUE, row.names = 1)
ps_dada2 <- import_dada2(
seq_tab = seq_tab,
tax_tab = tax_tab,
sam_tab = sam_tab)
ps_dada2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 232 taxa and 20 samples ]
## sample_data() Sample Data: [ 20 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 232 taxa by 6 taxonomic ranks ]
## refseq() DNAStringSet: [ 232 reference sequences ]- Converting the qiime2 output of dada2 into phyloseq object
otuqza_file <- system.file(
"extdata", "table.qza",
package = "MicrobiomeAnalysis")
taxaqza_file <- system.file(
"extdata", "taxonomy.qza",
package = "MicrobiomeAnalysis")
sample_file <- system.file(
"extdata", "sample-metadata.tsv",
package = "MicrobiomeAnalysis")
treeqza_file <- system.file(
"extdata", "tree.qza",
package = "MicrobiomeAnalysis")
ps_qiime2 <- import_qiime2(
otu_qza = otuqza_file, taxa_qza = taxaqza_file,
sam_tab = sample_file, tree_qza = treeqza_file
)
ps_qiime2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 770 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 9 sample variables ]
## tax_table() Taxonomy Table: [ 770 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 770 tips and 768 internal nodes ]- Convertings inputs into SummarizedExperiment object
data("Zeybel_2022_protein")
assay <- SummarizedExperiment::assay(Zeybel_2022_protein) %>%
data.frame()
rowData <- SummarizedExperiment::rowData(Zeybel_2022_protein) %>%
data.frame()
colData <- SummarizedExperiment::colData(Zeybel_2022_protein) %>%
data.frame()
metadata <- list(lab="hua", type="protein")
assay <- assay[1:10, 1:10]
se_protein <- import_SE(
object = assay,
rowdata = rowData,
coldata = colData,
metadata = metadata)
se_protein## class: SummarizedExperiment
## dim: 10 10
## metadata(2): lab type
## assays(1): ''
## rownames(10): IL8 VEGFA ... uPA IL6
## rowData names(3): ProteinID LOD prop
## colnames(10): P101001 P101003 ... P101013 P101016
## colData names(47): PatientID Gender ... Right_leg_fat_free_mass Right_leg_total_body_water3.3 Extracting specific levels
- Extracting “Genus” levels phyloseq object
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 66 taxa and 20 samples ]
## sample_data() Sample Data: [ 20 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 66 taxa by 7 taxonomic ranks ]3.4 Summarizing specific levels
- Phyloseq object contains from Kingdom to the the specific taxonomic levels (Phylum)
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 66 taxa and 20 samples ]
## sample_data() Sample Data: [ 20 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 66 taxa by 1 taxonomic ranks ]3.5 Data Transformation
7 methods to transform individual values (by individual value).
“log10”, the transformation is log10(object), and if the data contains zeros the transformation is log10(1 + object).
“log10p”, the transformation is log10(1 + object).
“log2”, the transformation is log2(object), and if the data contains zeros the transformation is log2(1 + object).
“log2p”, the transformation is log2(1 + object).
“SquareRoot”, the transformation is Square Root.
“CubicRoot”, the transformation is Cubic Root.
“logit”, the transformation is Zero-inflated Logit Transformation (Does not work well for microbiome data).
Here is for phyloseq-class.
data("Zeybel_2022_gut")
ps_transform <- transform_abundances(
object = Zeybel_2022_gut,
level = "Phylum",
transform = "log10p")
head(ps_transform@otu_table@.Data[, 1:5], 3)## P101003 P101007 P101010 P101012 P101018
## p__Actinobacteria -1.651096 -2.0986943 -2.5452009 -1.6773074 -1.8931057
## p__Bacteroidetes -0.330115 -0.1437857 -0.3379679 -0.1574846 -0.1295813
## p__Chloroflexi 0.000000 0.0000000 0.0000000 0.0000000 0.0000000- Here is for
SummarizedExperiment-class.
data("Zeybel_2022_protein")
se_transform <- transform_abundances(
object = Zeybel_2022_protein,
transform = "SquareRoot")
head(assay(se_transform)[, 1:5], 3)## P101001 P101003 P101004 P101007 P101009
## IL8 2.246691 2.063092 2.170020 2.400327 2.168238
## VEGFA 3.344589 3.302819 3.246115 3.321395 3.258289
## CD8A 3.398494 3.185073 3.132900 3.262700 3.0751313.6 Data Imputation
11 methods to impute missing value (NAs or Zeros)
“LOD”: specific Limit Of Detection which provides by user.
“half_min”: half minimal values across samples except zero.
“median”: median values across samples except zero.
“mean”: mean values across samples except zero.
“min”: minimal values across samples except zero.
“knn”: k-nearest neighbors samples.
“rf”: nonparametric missing value imputation using Random Forest.
“global_mean”: a normal distribution with a mean that is down-shifted from the sample mean and a standard deviation that is a fraction of the standard deviation of the sample distribution.
“svd”: missing values imputation based Singular value decomposition.
“QRILC”: missing values imputation based quantile regression. (default: “none”).
phyloseq-class as inputs
data("Zeybel_2022_gut")
ps_impute <- impute_abundance(
object = Zeybel_2022_gut,
level = "Phylum",
group = "LiverFatClass",
ZerosAsNA = TRUE,
RemoveNA = TRUE,
cutoff = 20,
method = "knn")
head(ps_impute@otu_table@.Data[, 1:5], 3)## P101003 P101007 P101010 P101012 P101018
## p__Actinobacteria 0.0223308 0.0079672 0.0028497 0.0210229 0.0127907
## p__Bacteroidetes 0.4676113 0.7181486 0.4592320 0.6958497 0.7420252
## p__Firmicutes 0.4818712 0.2715033 0.4577191 0.2712132 0.1701021- Inputs are from
SummarizedExperiment-class.
data("Zeybel_2022_protein")
se_impute <- impute_abundance(
object = Zeybel_2022_protein,
group = "LiverFatClass",
ZerosAsNA = TRUE,
RemoveNA = TRUE,
cutoff = 20,
method = "knn")
head(assay(se_impute)[, 1:5], 3)## P101001 P101003 P101004 P101007 P101009
## IL8 5.047325 4.25600 4.70867 5.76131 4.70094
## VEGFA 11.186140 10.90848 10.53712 11.03153 10.61631
## CD8A 11.549635 10.14454 9.81491 10.64507 9.456273.7 Data Normalization
Normalizing the OTU_table in phyloseq-class object sample by sample to reduce the effects of systematic differences such as library size (by sample).
“rarefy”: random subsampling counts to the smallest library size in the data set.
“TSS”: total sum scaling, also referred to as “relative abundance”, the abundances were normalized by dividing the corresponding sample library size.
“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.
“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.
“CSS”: cumulative sum scaling, calculates scaling factors as the cumulative sum of gene abundances up to a data-derived threshold.
“CLR”: centered log-ratio normalization.
“CPM”: pre-sample normalization of the sum of the values to 1e+06.
data("caporaso")
ps_norm <- normalize(
object = caporaso,
method = "TSS")
head(ps_norm@otu_table@.Data[, 1:5], 3)## L1S140 L1S208 L1S8 L1S281 L3S242
## New.CleanUp.ReferenceOTU647 0 0.0000000000 0 0 0
## 14030 0 0.0000000000 0 0 0
## New.CleanUp.ReferenceOTU858 0 0.0001013993 0 0 03.8 Data Scaling
Data scaling adjusts each variable/feature by a scaling factor computed based on the dispersion of the variable (by variable/feature).
“mean_center”: values minus mean statistic.
“zscore”: mean-centered and divided by the standard deviation of each variable.
“pareto”: mean-centered and divided by the square root of the standard deviation of each variable.
“range”: mean-centered and divided by the range of each variable. (default: “none”).
phyloseq-class as inputs
data("enterotypes_arumugam")
ps_scale <- scale_variables(
object = enterotypes_arumugam,
level = "Phylum",
method = "range")
head(ps_scale@otu_table@.Data[, 1:5], 3)## AM-AD-1 AM-AD-2 AM-F10-T1 AM-F10-T2 DA-AD-1
## Acidobacteria 0.00000000 0.00000000 0.0000000 0.000000000 0.0000000
## Actinobacteria 0.03379937 -0.05187261 0.0180014 0.882710081 -0.1132282
## Bacteroidetes -0.21758868 -0.21769150 -0.0870175 0.004106405 0.4083952Inputs are from SummarizedExperiment-class.
data("Zeybel_2022_protein")
se_impute <- impute_abundance(
object = Zeybel_2022_protein,
group = "LiverFatClass",
ZerosAsNA = TRUE,
RemoveNA = TRUE,
cutoff = 20,
method = "knn")
se_scale <- scale_variables(
se_impute,
method = "zscore")
head(assay(se_scale)[, 1:5], 3)## P101001 P101003 P101004 P101007 P101009
## IL8 0.1612462 -1.49334575 -0.5468520 1.6541269 -0.5630148
## VEGFA 0.8041564 0.08413878 -0.8788582 0.4032275 -0.6735057
## CD8A 2.8143331 0.18882517 -0.4271091 1.1240968 -1.09725043.9 Data Trimming
Trimming samples or features whose prevalence is less than threshold
“both”, prevalence of features and samples more than cutoff.
“feature”, prevalence of features more than cutoff.
“feature_group”, prevalence of features more than cutoff by groups.
“sample”, prevalence of samples more than cutoff.
phyloseq-class as inputs
data("Zeybel_2022_gut")
ps_trim <- trim_prevalence(
Zeybel_2022_gut,
group = "LiverFatClass",
level = "Phylum",
cutoff = 0.1,
trim = "feature_group")
ps_trim## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 5 taxa and 42 samples ]
## sample_data() Sample Data: [ 42 samples by 46 sample variables ]
## tax_table() Taxonomy Table: [ 5 taxa by 3 taxonomic ranks ]Inputs are from SummarizedExperiment-class.
data("Zeybel_2022_protein")
se_trim <- trim_prevalence(
Zeybel_2022_protein,
cutoff = 0.99,
trim = "both")
se_trim## class: SummarizedExperiment
## dim: 66 54
## metadata(0):
## assays(1): ''
## rownames(66): IL8 VEGFA ... TNFB CSF_1
## rowData names(3): ProteinID LOD prop
## colnames(54): P101001 P101003 ... P101095 P101096
## colData names(47): PatientID Gender ... Right_leg_fat_free_mass Right_leg_total_body_water3.10 Data Filtering
Filtering feature who is low relative abundance or unclassified (Ref: (Thingholm et al. 2019))
Feature is more than Mean relative abundance across all samples;
Feature is more than Minimum relative abundance at least one sample.
phyloseq-class as inputs
data("Zeybel_2022_gut")
Zeybel_2022_gut_counts <- phyloseq::transform_sample_counts(
Zeybel_2022_gut, function(x) {round(x * 10^7)})
# absolute abundance
ps_filter_absolute <- filter_abundance(
object = Zeybel_2022_gut_counts,
level = "Genus",
cutoff_mean = 100,
cutoff_one = 1000,
unclass = FALSE)
ps_filter_absolute## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 94 taxa and 42 samples ]
## sample_data() Sample Data: [ 42 samples by 46 sample variables ]
## tax_table() Taxonomy Table: [ 94 taxa by 7 taxonomic ranks ]# relative abundance
ps_filter_relative <- filter_abundance(
object = Zeybel_2022_gut,
level = "Genus",
cutoff_mean = 1e-04,
cutoff_one = 1e-03,
unclass = TRUE)
ps_filter_relative## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 67 taxa and 42 samples ]
## sample_data() Sample Data: [ 42 samples by 46 sample variables ]
## tax_table() Taxonomy Table: [ 67 taxa by 7 taxonomic ranks ]Inputs are from SummarizedExperiment-class.
data("Zeybel_2022_protein")
se_filter <- filter_abundance(
object = Zeybel_2022_protein,
cutoff_mean = 5,
cutoff_one = 8)
se_filter## class: SummarizedExperiment
## dim: 39 54
## metadata(0):
## assays(1): ''
## rownames(39): VEGFA CD8A ... STAMBP CSF_1
## rowData names(3): ProteinID LOD prop
## colnames(54): P101001 P101003 ... P101095 P101096
## colData names(47): PatientID Gender ... Right_leg_fat_free_mass Right_leg_total_body_water3.11 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 2023-08-16
## rstudio 2023.06.1+524 Mountain Hydrangea (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
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##
## [1] /Users/zouhua/Library/R/x86_64/4.1/library
## [2] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
## ─ Python configuration ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## python: /Users/zouhua/opt/anaconda3/bin/python3.9
## libpython: /Users/zouhua/opt/anaconda3/lib/libpython3.9.dylib
## pythonhome: /Users/zouhua/opt/anaconda3:/Users/zouhua/opt/anaconda3
## version: 3.9.16 | packaged by conda-forge | (main, Feb 1 2023, 21:42:20) [Clang 14.0.6 ]
## numpy: /Users/zouhua/opt/anaconda3/lib/python3.9/site-packages/numpy
## numpy_version: 1.23.3
##
## NOTE: Python version was forced by use_python function
##
## ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────References
Thingholm, Louise B, Malte C Rühlemann, Manja Koch, Brie Fuqua, Guido Laucke, Ruwen Boehm, Corinna Bang, et al. 2019. “Obese Individuals with and Without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition.” Cell Host & Microbe 26 (2): 252–64.