Chapter 8 Differential Analysis
Loading packages
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: [ 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 ]- 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: [ 132 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 2 sample variables ]
## tax_table() Taxonomy Table: [ 132 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)
metaphlan2_ps_lefse <- run_lefse2(
ps = metaphlan2_ps_species_filter_trim,
group = "Group",
group_names = c("AA", "BB"),
norm = "CPM",
lda_cutoff = 2)
head(metaphlan2_ps_lefse)## TaxaID Block LDA_Score Enrichment EffectSize Pvalue Log2FoldChange (Median)\nAA_vs_BB
## 1 s__Adlercreutzia_equolifaciens 7_AA vs 15_BB 3.160778 BB 1.577193 0.03675238 NA
## 2 s__Bacteroides_thetaiotaomicron 7_AA vs 15_BB -4.688237 AA 4.204038 0.00531109 5.419566
## 3 s__Bifidobacterium_adolescentis 7_AA vs 15_BB 4.755989 BB 3.883638 0.01273201 NA
## 4 s__Bifidobacterium_longum 7_AA vs 15_BB 4.764078 BB 2.466823 0.02596373 -4.652794
## 5 s__Clostridium_asparagiforme 7_AA vs 15_BB -2.999903 AA 1.604847 0.03886151 NA
## 6 s__Collinsella_aerofaciens 7_AA vs 15_BB 4.214636 BB 3.122592 0.03312054 NA
## Median Abundance\n(All) Median Abundance\nAA Median Abundance\nBB Log2FoldChange (Mean)\nAA_vs_BB Mean Abundance\n(All) Mean Abundance\nAA
## 1 0.000 0.0000 0.000 NA 908.0076 0.000
## 2 2879.516 46627.3399 1089.403 3.196193 24165.2943 61556.260
## 3 0.000 0.0000 3849.789 NA 36307.7414 0.000
## 4 11054.184 1204.0647 30288.685 -3.714249 41325.0829 4459.348
## 5 0.000 267.2502 0.000 4.254960 502.7677 1420.679
## 6 7749.743 0.0000 13231.406 -3.238523 12772.4540 1891.272
## Mean Abundance\nBB Occurrence (100%)\n(All) Occurrence (100%)\nAA Occurrence (100%)\nBB Odds Ratio (95% CI)
## 1 1331.745 31.82 0.00 46.67 <NA>
## 2 6716.177 86.36 100.00 80.00 0.062 (-5.4;5.5)
## 3 53251.354 40.91 0.00 60.00 <NA>
## 4 58529.092 81.82 71.43 86.67 260 (270;250)
## 5 74.409 27.27 57.14 13.33 0.0067 (-9.8;9.8)
## 6 17850.339 63.64 42.86 73.33 30 (37;23)- 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 Log2FoldChange (Median)\nAA_vs_BB
## 1 s__Acidaminococcus_fermentans 7_AA vs 15_BB Nonsignif 0.1545096 49.0 0.75333110 0.9039973 NA
## 2 s__Acidaminococcus_intestini 7_AA vs 15_BB Nonsignif 0.1177459 54.0 0.90599343 0.9722856 NA
## 3 s__Adlercreutzia_equolifaciens 7_AA vs 15_BB Nonsignif 1.6614672 28.0 0.04076802 0.3520563 NA
## 4 s__Alistipes_finegoldii 7_AA vs 15_BB Nonsignif 0.2935476 75.0 0.08188173 0.4793154 NA
## 5 s__Alistipes_indistinctus 7_AA vs 15_BB Nonsignif 0.3308118 68.0 0.17792767 0.4793154 NA
## 6 s__Alistipes_onderdonkii 7_AA vs 15_BB Nonsignif 0.3147638 66.5 0.29869756 0.5730690 NA
## Median Abundance\n(All) Median Abundance\nAA Median Abundance\nBB Log2FoldChange (Rank)\nAA_vs_BB Mean Rank Abundance\nAA
## 1 0 0.0000000 0 -0.09269949 11.00
## 2 0 0.0000000 0 0.03870725 11.71
## 3 0 0.0000000 0 -0.71479501 8.00
## 4 0 0.0027362 0 0.55679725 14.71
## 5 0 0.0000000 0 0.38896713 13.71
## 6 0 0.0002823 0 0.35298403 13.50
## Mean Rank Abundance\nBB Occurrence (100%)\n(All) Occurrence (100%)\nAA Occurrence (100%)\nBB Odds Ratio (95% CI)
## 1 11.73 18.18 14.29 20.00 1.5 (2.4;0.69)
## 2 11.40 13.64 14.29 13.33 0.62 (-0.32;1.6)
## 3 13.13 31.82 0.00 46.67 <NA>
## 4 10.00 40.91 57.14 33.33 0.0025 (-12;12)
## 5 10.47 27.27 42.86 20.00 0.0085 (-9.3;9.4)
## 6 10.57 45.45 57.14 40.00 0.37 (-1.6;2.3)- 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
8.6 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)
##
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##
## [1] /Users/zouhua/Library/R/x86_64/4.1/library
## [2] /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.