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 Chapter 10 to see more details.
8.1 Filtering & Trimming
We suggest that filtering taxa with low abundance (the summarized value under cutoff: 1e-4) 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 < 1e-4)
- filter by sum relative abundance
metaphlan2_ps_species_filter <- run_filter(ps = metaphlan2_ps_LOD_species_remove_BRS,
cutoff = 1e-4,
unclass = TRUE)
metaphlan2_ps_species_filter ## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 171 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 171 taxa by 7 taxonomic ranks ]- filter by following two criterion (Thingholm et al. 2019)
Species from taxonomic profiles were retained for further analysis if their mean relative abundance exceeded 0.005 (0.5%) across the dataset with a minimum abundance of 0.05 (5%) in at least one sample and non-zero abundance in at least 60% of samples.
Mean relative abundance: 0.005;
Minimum relative abundance: 0.05;
Here, we use 0.01 (the 1e-4 regarded as 0.01 compared to the Referece because Metaphlan2 data had been divided 100).
metaphlan2_ps_species_filter2 <- run_filter2(ps = metaphlan2_ps_LOD_species_remove_BRS,
cutoff_mean = 1e-04,
cutoff_one = 1e-03,
unclass = TRUE)
metaphlan2_ps_species_filter2## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 144 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 144 taxa by 7 taxonomic ranks ]8.1.2 Trimming the taxa with low occurrence less than threshold
metaphlan2_ps_species_filter_trim <- run_trim(object = metaphlan2_ps_species_filter,
cutoff = 0.1,
trim = "feature")
metaphlan2_ps_species_filter_trim## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 149 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 149 taxa by 7 taxonomic ranks ]8.2 Liner discriminant analysis (LDA) effect size (LEfSe)
- Calculation
metaphlan2_ps_lefse <- run_lefse(
ps = metaphlan2_ps_species_filter_trim,
group = "Group",
group_names = c("AA", "BB"),
norm = "CPM",
Lda = 2)
head(metaphlan2_ps_lefse)## TaxaID Block Enrichment LDA_Score EffectSize Log2FoldChange (Median)\nAA_vs_BB
## 1 s__Adlercreutzia_equolifaciens 7_AA vs 15_BB BB 2.893801 1.738400 NA
## 2 s__Bacteroides_thetaiotaomicron 7_AA vs 15_BB AA -4.411141 4.181157 5.420162
## 3 s__Bifidobacterium_adolescentis 7_AA vs 15_BB BB 4.480463 4.091377 NA
## 4 s__Bifidobacterium_longum 7_AA vs 15_BB BB 4.484013 2.565536 -4.652510
## 5 s__Collinsella_aerofaciens 7_AA vs 15_BB BB 3.833332 3.209757 NA
## 6 s__Dorea_longicatena 7_AA vs 15_BB BB 3.677089 3.354326 NA
## Median Abundance\n(All) Median Abundance\nAA Median Abundance\nBB Log2FoldChange (Mean)\nAA_vs_BB Mean Abundance\n(All)
## 1 0.0000 0.000 0.000 NA 906.9516
## 2 2878.5342 46622.430 1088.838 3.197793 24149.8300
## 3 0.0000 0.000 3847.794 NA 36283.4237
## 4 11051.4886 1203.026 30256.593 -3.715431 41286.5969
## 5 7740.2322 0.000 13224.549 -3.239567 12751.7136
## 6 828.3582 0.000 8752.040 -7.351468 7123.5161
## Mean Abundance\nAA Mean Abundance\nBB Occurrence (100%)\n(All) Occurrence (100%)\nAA Occurrence (100%)\nBB
## 1 0.00000 1330.196 31.82 0.00 46.67
## 2 61529.78923 6705.849 86.36 100.00 80.00
## 3 0.00000 53215.688 40.91 0.00 60.00
## 4 4451.67148 58476.229 81.82 71.43 86.67
## 5 1886.89926 17821.960 63.64 42.86 73.33
## 6 63.79319 10418.053 63.64 42.86 73.33
## Odds Ratio (95% CI)
## 1 <NA>
## 2 0.062 (-5.4;5.5)
## 3 <NA>
## 4 260 (270;250)
## 5 30 (37;23)
## 6 1.2e+14 (1.2e+14;1.2e+14)- Visualization
# # don't run this code when you do lefse in reality
# metaphlan2_ps_lefse$LDA_Score <- metaphlan2_ps_lefse$LDA_Score * 1000
plot_lefse(
da_res = metaphlan2_ps_lefse,
x_index = "LDA_Score",
x_index_cutoff = 1,
group_color = c("green", "red"))
Figure 8.1: Lefse analysis
- how to plot cladogram using lefse results to see cladogram lefse
8.3 Wilcoxon Rank-Sum test
- Calculation
metaphlan2_ps_wilcox <- run_wilcox(
ps = metaphlan2_ps_species_filter_trim,
group = "Group",
group_names = c("AA", "BB"))
head(metaphlan2_ps_wilcox)## TaxaID Block Enrichment EffectSize Statistic Pvalue AdjustedPvalue
## 1 s__Acidaminococcus_fermentans 7_AA vs 15_BB Nonsignif 0 56.0 0.75333110 1.0000000
## 2 s__Acidaminococcus_intestini 7_AA vs 15_BB Nonsignif 0 53.0 1.00000000 1.0000000
## 3 s__Adlercreutzia_equolifaciens 7_AA vs 15_BB Nonsignif 0 77.0 0.04076802 0.7081309
## 4 s__Alistipes_finegoldii 7_AA vs 15_BB Nonsignif 0 50.0 0.87432279 1.0000000
## 5 s__Alistipes_indistinctus 7_AA vs 15_BB Nonsignif 0 44.0 0.47245703 0.9412659
## 6 s__Alistipes_onderdonkii 7_AA vs 15_BB Nonsignif 0 47.5 0.72903449 1.0000000
## Log2FoldChange (Median)\nAA_vs_BB Median Abundance\n(All) Median Abundance\nAA Median Abundance\nBB
## 1 NA 0 0.0000000 0
## 2 NA 0 0.0000000 0
## 3 NA 0 0.0000000 0
## 4 NA 0 -1.0437656 0
## 5 NA 0 0.0000000 0
## 6 NA 0 -0.8654574 0
## Log2FoldChange (Rank)\nAA_vs_BB Mean Rank Abundance\nAA Mean Rank Abundance\nBB Occurrence (100%)\n(All) Occurrence (100%)\nAA
## 1 0.09054689 12.00 11.27 18.18 14.29
## 2 0.01252347 11.57 11.47 13.64 14.29
## 3 0.60384051 15.00 9.87 31.82 0.00
## 4 -0.06705533 11.14 11.67 40.91 57.14
## 5 -0.23018269 10.29 12.07 27.27 42.86
## 6 -0.13275521 10.79 11.83 45.45 57.14
## Occurrence (100%)\nBB Odds Ratio (95% CI)
## 1 20.00 1 (1.1;0.96)
## 2 13.33 0.69 (-0.038;1.4)
## 3 46.67 <NA>
## 4 33.33 0.25 (-2.5;3)
## 5 20.00 0.3 (-2;2.6)
## 6 40.00 0.64 (-0.23;1.5)- Volcano
plot_volcano(
da_res = metaphlan2_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 = 4,
taxa_name = "s__Megamonas_rupellensis")
Figure 8.2: Wilcoxon Rank-Sum test
8.4 Dominant taxa
Display the significant taxa with selection using boxplot.
plot_topN_boxplot(
ps = metaphlan2_ps_species_filter_trim,
da_res = metaphlan2_ps_wilcox,
x_index = "Log2FoldChange (Rank)\nAA_vs_BB",
x_index_cutoff = 0.5,
y_index = "Pvalue",
y_index_cutoff = 0.05,
topN = 4,
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 = metaphlan2_ps_species_filter_trim,
group = "Group",
group_names = c("AA", "BB"),
da_method = c("wilcox", "limma_voom", "ttest"))
names(multiple_res)## [1] "wilcox" "limma_voom" "ttest"- plot results
plot_multiple_DA(
Multip_DA_res = multiple_res,
x_index_list = c("Log2FoldChange (Rank)\nAA_vs_BB",
"logFC",
"Log2FoldChange (Mean)\nAA_vs_BB"),
x_index_cutoff = 0,
y_index = "Pvalue",
y_index_cutoff = 0.05,
cellwidth = 35,
cellheight = 10,
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 2023-04-12
## rstudio 2022.07.2+576 Spotted Wakerobin (desktop)
## pandoc 2.19.2 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
##
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## sjmisc 2.8.9 2021-12-03 [1] CRAN (R 4.1.0)
## 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 1.7.8 2022-07-11 [1] CRAN (R 4.1.2)
## stringr 1.4.1 2022-08-20 [1] CRAN (R 4.1.2)
## SummarizedExperiment 1.24.0 2021-10-26 [1] Bioconductor
## survival 3.4-0 2022-08-09 [1] CRAN (R 4.1.2)
## systemfonts 1.0.4 2022-02-11 [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 * 3.1.8 2022-07-22 [1] CRAN (R 4.1.2)
## 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)
## 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)
## uuid 1.0-3 2021-11-01 [1] CRAN (R 4.1.0)
## vctrs 0.5.1 2022-11-16 [1] CRAN (R 4.1.2)
## 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 0.34 2022-10-18 [1] CRAN (R 4.1.2)
## XMAS2 * 2.1.8.7 2023-01-06 [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 2.3.6 2022-10-18 [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
##
## ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────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.