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"))
Lefse analysis (16s example)

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)
Wilcoxon Rank-Sum test (16s example)

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")
Dominant Taxa

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)
Multiple DA results

Figure 8.4: Multiple DA results

8.6 Systematic Information

devtools::session_info()
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##  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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##  psych                  2.2.5    2022-05-10 [1] CRAN (R 4.1.2)
##  pulsar                 0.3.7    2020-08-07 [1] CRAN (R 4.1.0)
##  purrr                  0.3.4    2020-04-17 [1] CRAN (R 4.1.0)
##  qgraph                 1.9.2    2022-03-04 [1] CRAN (R 4.1.2)
##  R6                     2.5.1    2021-08-19 [1] CRAN (R 4.1.0)
##  rbibutils              2.2.7    2021-12-07 [1] CRAN (R 4.1.0)
##  RColorBrewer           1.1-2    2014-12-07 [1] CRAN (R 4.1.0)
##  Rcpp                 * 1.0.8.2  2022-03-11 [1] CRAN (R 4.1.2)
##  RcppZiggurat           0.1.6    2020-10-20 [1] CRAN (R 4.1.0)
##  RCurl                  1.98-1.6 2022-02-08 [1] CRAN (R 4.1.2)
##  Rdpack                 2.2      2022-03-19 [1] CRAN (R 4.1.2)
##  readxl               * 1.4.0    2022-03-28 [1] CRAN (R 4.1.2)
##  remotes                2.4.2    2021-11-30 [1] CRAN (R 4.1.0)
##  reshape2               1.4.4    2020-04-09 [1] CRAN (R 4.1.0)
##  Rfast                  2.0.6    2022-02-16 [1] CRAN (R 4.1.2)
##  rhdf5                  2.38.1   2022-03-10 [1] Bioconductor
##  rhdf5filters           1.6.0    2021-10-26 [1] Bioconductor
##  Rhdf5lib               1.16.0   2021-10-26 [1] Bioconductor
##  rlang                  1.0.2    2022-03-04 [1] CRAN (R 4.1.2)
##  rmarkdown              2.14     2022-04-25 [1] CRAN (R 4.1.2)
##  rootSolve              1.8.2.3  2021-09-29 [1] CRAN (R 4.1.0)
##  rpart                  4.1.16   2022-01-24 [1] CRAN (R 4.1.2)
##  rprojroot              2.0.2    2020-11-15 [1] CRAN (R 4.1.0)
##  RSQLite                2.2.10   2022-02-17 [1] CRAN (R 4.1.2)
##  rstatix                0.7.0    2021-02-13 [1] CRAN (R 4.1.0)
##  rstudioapi             0.13     2020-11-12 [1] CRAN (R 4.1.0)
##  S4Vectors              0.32.3   2021-11-21 [1] Bioconductor
##  sandwich               3.0-1    2021-05-18 [1] CRAN (R 4.1.0)
##  sass                   0.4.0    2021-05-12 [1] CRAN (R 4.1.0)
##  scales                 1.1.1    2020-05-11 [1] CRAN (R 4.1.0)
##  sessioninfo            1.2.2    2021-12-06 [1] CRAN (R 4.1.0)
##  shape                  1.4.6    2021-05-19 [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                1.7.6    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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