Chapter 8 Differential Analysis
Loading packages
library(XMAS2)
library(dplyr)
library(tibble)
library(phyloseq)
library(ggplot2)
library(ggpubr)There are more than 10 approaches to perform differential analysis. Here, we choose two of them and recommend users going to XMAS2: Chapter 11 to see more details.
8.1 Filtering and trimming
We suggest that filtering taxa with low abundance (the summarized value under cutoff: 10) and trimming taxa with low prevalence (default: 0.1).
8.1.1 Filtering the low relative abundance or unclassified taxa by the threshold (total counts < 10)
dada2_ps_rare_genus_filter <- run_filter(ps = dada2_ps_rare_genus,
cutoff = 10,
unclass = TRUE)
dada2_ps_rare_genus_filter ## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 149 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 149 taxa by 6 taxonomic ranks ]8.1.2 Trimming the taxa with low occurrence less than threshold
dada2_ps_rare_genus_filter_trim <- run_trim(object = dada2_ps_rare_genus_filter,
cutoff = 0.1,
trim = "feature")
dada2_ps_rare_genus_filter_trim## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 122 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 1 sample variables ]
## tax_table() Taxonomy Table: [ 122 taxa by 6 taxonomic ranks ]Finally, we obtained the final phyloseq-class object dada2_ps_rare_genus_filter_trim and changed its name.
8.2 Liner discriminant analysis (LDA) effect size (LEfSe)
- Calculation
dada2_ps_lefse <- run_lefse(
ps = dada2_ps_rare_genus_filter_trim,
group = "Group",
group_names = c("AA", "BB"),
norm = "CPM",
Lda = 2)
head(dada2_ps_lefse)## TaxaID Block Enrichment LDA_Score EffectSize Log2FoldChange (Median)\nAA_vs_BB
## 1 g__Parasutterella 9_AA vs 14_BB AA -3.622720 2.337478 4.400391
## 2 g__Ruminococcaceae_UCG_004 9_AA vs 14_BB AA -2.786635 2.323866 NA
## 3 g__Intestinibacter 9_AA vs 14_BB BB 3.180391 2.707925 NA
## 4 g__Clostridium_sensu_stricto_1 9_AA vs 14_BB BB 3.657070 2.999010 NA
## 5 g__Romboutsia 9_AA vs 14_BB BB 3.689270 3.101747 -6.855390
## 6 g__Lactobacillus 9_AA vs 14_BB BB 4.080945 2.535860 -3.319206
## Median Abundance\n(All) Median Abundance\nAA Median Abundance\nBB Log2FoldChange (Mean)\nAA_vs_BB Mean Abundance\n(All)
## 1 152.0946 1075.68521 50.93725 4.382363 3626.399
## 2 222.5414 2450.05654 0.00000 2.352990 1382.304
## 3 380.1043 0.00000 1210.74287 -2.077598 1782.122
## 4 224.7329 0.00000 985.98421 -4.839795 5556.933
## 5 2186.0380 50.35627 5830.84783 -3.320093 6940.723
## 6 1281.2951 510.55140 5095.89137 -5.210661 19394.730
## Mean Abundance\nAA Mean Abundance\nBB Occurrence (100%)\n(All) Occurrence (100%)\nAA Occurrence (100%)\nBB
## 1 8624.2116 413.5201 65.22 88.89 50.00
## 2 2708.0076 530.0652 52.17 77.78 35.71
## 3 601.9396 2540.8102 60.87 22.22 85.71
## 4 311.7956 8928.8074 60.87 22.22 85.71
## 5 1072.6678 10713.0444 78.26 55.56 92.86
## 6 845.7532 31319.0727 86.96 77.78 92.86
## Odds Ratio (95% CI)
## 1 0.0051 (-10;10)
## 2 0.15 (-3.5;3.8)
## 3 3.7 (6.3;1.1)
## 4 6500 (6500;6500)
## 5 63 (71;55)
## 6 3.5e+08 (3.5e+08;3.5e+08)- Visualization
# # don't run this code when you do lefse in reality
# dada2_ps_lefse$LDA_Score <- dada2_ps_lefse$LDA_Score * 1000
plot_lefse(
da_res = dada2_ps_lefse,
x_index = "LDA_Score",
x_index_cutoff = 2,
group_color = c("green", "red"))
Figure 8.1: Lefse analysis (16s example)
8.3 Wilcoxon Rank-Sum test
- Calculation
dada2_ps_wilcox <- run_wilcox(
ps = dada2_ps_rare_genus_filter_trim,
group = "Group",
group_names = c("AA", "BB"))
head(dada2_ps_wilcox)## TaxaID Block Enrichment EffectSize Statistic Pvalue AdjustedPvalue Log2FoldChange (Median)\nAA_vs_BB
## 1 g__Acidaminococcus 9_AA vs 14_BB Nonsignif 70.56349 63.5 1.0000000 1.0000000 NA
## 2 g__Actinomyces 9_AA vs 14_BB Nonsignif 39.65079 44.5 0.2556616 0.7335420 -0.9577718
## 3 g__Adlercreutzia 9_AA vs 14_BB Nonsignif 23.17460 44.0 0.1980169 0.7335420 NA
## 4 g__Agathobacter 9_AA vs 14_BB Nonsignif 1222.58730 71.0 0.6293971 0.9141243 0.1524904
## 5 g__Akkermansia 9_AA vs 14_BB Nonsignif 104.78571 64.5 0.9304707 0.9702344 NA
## 6 g__Alistipes 9_AA vs 14_BB Nonsignif 23.03175 67.0 0.8239942 0.9308083 -0.1085245
## Median Abundance\n(All) Median Abundance\nAA Median Abundance\nBB Log2FoldChange (Rank)\nAA_vs_BB Mean Rank Abundance\nAA
## 1 0 0 0.0 0.01201252 12.06
## 2 30 26 50.5 -0.42227633 9.94
## 3 0 0 9.0 -0.43387758 9.89
## 4 316 329 296.0 0.17342686 12.89
## 5 0 0 0.0 0.03358045 12.17
## 6 64 64 69.0 0.08724541 12.44
## Mean Rank Abundance\nBB Occurrence (100%)\n(All) Occurrence (100%)\nAA Occurrence (100%)\nBB Odds Ratio (95% CI)
## 1 11.96 21.74 22.22 21.43 0.67 (-0.11;1.5)
## 2 13.32 82.61 88.89 78.57 4.1 (6.8;1.3)
## 3 13.36 43.48 33.33 50.00 8.6 (13;4.3)
## 4 11.43 65.22 55.56 71.43 0.39 (-1.4;2.2)
## 5 11.89 21.74 22.22 21.43 1.5 (2.3;0.71)
## 6 11.71 73.91 77.78 71.43 0.88 (0.64;1.1)- Volcano
plot_volcano(
da_res = dada2_ps_wilcox,
group_names = c("AA", "BB"),
x_index = "Log2FoldChange (Rank)\nAA_vs_BB",
x_index_cutoff = 0.5,
y_index = "Pvalue",
y_index_cutoff = 0.05,
group_color = c("red", "grey", "blue"),
topN = 5)
Figure 8.2: Wilcoxon Rank-Sum test (16s example)
8.4 Dominant taxa
Display the significant taxa with selection using boxplot.
plot_topN_boxplot(
ps = dada2_ps_rare_genus_filter_trim,
da_res = dada2_ps_wilcox,
x_index = "Log2FoldChange (Rank)\nAA_vs_BB",
x_index_cutoff = 0.5,
y_index = "Pvalue",
y_index_cutoff = 0.05,
topN = 5,
group = "Group")
Figure 8.3: Dominant Taxa
8.5 Multiple differential analysis by one function
here, we provide the run_multiple_da for obtaining the results list from multiple differential analysis methods.
multiple_res <- run_multiple_da(
ps = dada2_ps_rare_genus_filter_trim,
group = "Group",
group_names = c("AA", "BB"),
da_method = c("aldex", "limma_voom", "mbzinb", "omnibus"))## |------------(25%)----------(50%)----------(75%)----------|
## Start GMPR normalization ...
## Start Winsorization ...
## Perform filtering ...
## --A total of 102 taxa will be tested with a sample size of 23 !
## --Omnibus test is selected!
## --Dispersion is treated as a parameter of interest!
## Start testing ...
## 10 %
## 20 %
## 30 %
## 40 %
## 50 %
## 60 %
## 70 %
## 80 %
## 90 %
## 100%!
## Handle failed taxa using permutation test!
## Permutation test ....
## Completed!names(multiple_res)## [1] "aldex" "limma_voom" "mbzinb" "omnibus"- plot results
plot_multiple_DA(
Multip_DA_res = multiple_res,
x_index_list = c("EffectSize", "logFC", "mean.LFC", "abund.LFC.CompvarBB.est"),
x_index_cutoff = 1,
y_index = "AdjustedPvalue",
y_index_cutoff = 0.5,
cellwidth = 50,
cellheight = 15,
fontsize_number = 15)
Figure 8.4: Multiple DA results
8.6 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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## snow 0.4-4 2021-10-27 [1] CRAN (R 4.1.0)
## SpiecEasi * 1.1.2 2022-07-14 [1] Github (zdk123/SpiecEasi@c463727)
## SPRING 1.0.4 2022-08-03 [1] Github (GraceYoon/SPRING@3d641a4)
## stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.0)
## stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.0)
## SummarizedExperiment 1.24.0 2021-10-26 [1] Bioconductor
## survival 3.3-1 2022-03-03 [1] CRAN (R 4.1.2)
## testthat 3.1.2 2022-01-20 [1] CRAN (R 4.1.2)
## TH.data 1.1-0 2021-09-27 [1] CRAN (R 4.1.0)
## tibble * 3.1.6 2021-11-07 [1] CRAN (R 4.1.0)
## tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.1.2)
## tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.1.2)
## tmvnsim 1.0-2 2016-12-15 [1] CRAN (R 4.1.0)
## truncnorm 1.0-8 2018-02-27 [1] CRAN (R 4.1.0)
## usethis 2.1.5 2021-12-09 [1] CRAN (R 4.1.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
## vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.0)
## vegan 2.5-7 2020-11-28 [1] CRAN (R 4.1.0)
## VGAM 1.1-6 2022-02-14 [1] CRAN (R 4.1.2)
## WGCNA 1.71 2022-04-22 [1] CRAN (R 4.1.2)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.2)
## Wrench 1.12.0 2021-10-26 [1] Bioconductor
## xfun 0.30 2022-03-02 [1] CRAN (R 4.1.2)
## XMAS2 * 2.1.7.4 2022-08-09 [1] local
## XML 3.99-0.9 2022-02-24 [1] CRAN (R 4.1.2)
## 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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