Chapter 4 Data processing
he 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. |
Loading packages
This part has too may procedures and we only choose some of them. Please go to XMAS tutorial: Chapter 6 to see more approaches and details for being familiar with this part.
4.1 Rarefy otu counts
From previous results of quality evaluation, the sequence depth of samples are different which have effects on the downstream analysis. Here, choosing the rarefy (Normaliztion method: random subsampling counts to the smallest library size) to get the equal sample sums.
## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 891 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 891 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 891 tips and 888 internal nodes ]
## refseq() DNAStringSet: [ 891 reference sequences ]In addition, we could also perform some other normalization methods on the rarefied phyloseq object. By the way, we didn’t transform the data by using log algorithm because the count matrix is required by the following data analysis methods.
4.2 Extracting specific taxonomic level
## 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 ]4.3 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 2023-10-27
## rstudio 2023.09.0+463 Desert Sunflower (desktop)
## pandoc 3.1.1 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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## umap 0.2.10.0 2023-02-01 [2] CRAN (R 4.1.2)
## units 0.8-2 2023-04-27 [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)
## uuid 1.1-0 2022-04-19 [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)
## VennDiagram * 1.7.3 2022-04-12 [2] CRAN (R 4.1.2)
## VGAM 1.1-8 2023-03-09 [2] CRAN (R 4.1.2)
## viridis * 0.6.3 2023-05-03 [2] CRAN (R 4.1.2)
## viridisLite * 0.4.2 2023-05-02 [2] CRAN (R 4.1.2)
## vroom 1.6.3 2023-04-28 [2] CRAN (R 4.1.2)
## webshot 0.5.5 2023-06-26 [2] CRAN (R 4.1.3)
## WGCNA 1.72-1 2023-01-18 [2] CRAN (R 4.1.2)
## 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 2023-10-27 [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)
## zCompositions 1.4.0-1 2022-03-26 [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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