Chapter 12 Association Analysis

The potential relationships between microbiota and environmental factors such as clinical parameters are vital when scientists investigate the mechanism of diseases.

Here, we identify the associations between them by using spearman correlation analysis.

Outline of this Chapter:

12.1 Loading Packages

library(XMAS2)
library(dplyr)
library(tibble)
library(phyloseq)
library(pheatmap)

12.2 Importing Data

The Zeybel_Gut is from (Zeybel et al. 2022), containing gut and oral microbiota.

data("Zeybel_Gut")

Zeybel_Gut_genus <- summarize_taxa(ps = Zeybel_Gut,
       taxa_level = "Genus")
  • sample data in Zeybel_Gut object
zeybel_metadata <- sample_data(Zeybel_Gut_genus) %>% 
  data.frame()

head(zeybel_metadata)
##          Stage Metabolomics Proteomics GutMetagenomics OralMetagenomics LiverFatClass Gender AlcoholConsumption Smoker Age
## P101042 Before         Send       Send            Send             Send          Mild   Male                 No     No  33
## P101076 Before         Send       Send            Send             Send      Moderate   Male                 No     No  40
## P101095 Before         Send       Send            Send    Failed Sample      Moderate   Male                 No     No  29
## P101084 Before         Send       Send            Send             Send        Severe   Male                 No     No  45
## P101038 Before         Send       Send            Send             Send      Moderate Female                 No     No  43
## P101071 Before         Send       Send            Send             Send      Moderate   Male                 No    Yes  48
##               LF Sodium Potassium Creatinine Urea  UA ALT AST GGT  AP   TB Albumin  CK  TC HDL LDL Triglycerides Glucose
## P101042  6.45000    142       4.6       0.96   17 7.2  26  17  29 103 0.25    5.00  85 255  40 181           164      93
## P101076  9.30000    146       4.9       1.05   22 5.6  28  16  56  81 0.34    5.00 123 193  48 133           115     111
## P101095  9.90000    140       4.1       1.13   14 9.1  39  28  32  78 1.46    5.14 300 152  38 103            98      88
## P101084 18.15000    143       5.1       1.15   12 8.8  57  33  55  67 0.34    4.72 336 195  47 128           124      99
## P101038 15.93333    142       4.1       0.91    9 7.2  15  16  17  72 1.24    4.70 108 235  55 165           141     114
## P101071  8.10000    144       4.4       1.04   19 6.0  21  13  21  70 0.34    4.60  77 228  39 146           279     104
##         Insulin Weight  BMI  HR SBP DBP  WC  HC  TFM TFFM TTBW LAFM LAFFM LATBW RAFM RAFFM RATBW LLFM LLFFM LLTBW RLFM RLFFM
## P101042    11.4   84.5 31.0 101 120  70 100 109 13.9 32.0 44.8  1.3   3.6  44.8  1.2   3.6  44.8  3.4  10.9  44.8  3.4  11.1
## P101076    14.7   83.0 28.4  73 120  70  95 100 11.4 34.4 46.4  1.0   3.9  46.4  1.0   3.8  46.4  3.1  10.6  46.4  3.3  10.6
## P101095    10.3  123.1 38.9  73 110  70 118 128 23.8 43.2 61.5  2.7   5.0  61.5  2.3   5.1  61.5  4.8  15.5  61.5  5.5  15.2
## P101084    36.3  108.0 33.7  74 130  80 115 120 21.3 38.7 53.4  2.0   4.4  53.4  1.8   4.4  53.4  5.1  12.7  53.4  5.0  12.8
## P101038    24.6   98.6 37.6  85 130  80 100 124 23.4 28.9 52.0  3.1   2.9  52.0  2.9   2.8  52.0  8.6   8.7  52.0  8.7   8.7
## P101071    12.5  105.8 36.6  78 110  70 115 115 20.6 37.2 51.7  1.9   4.4  51.7  1.7   4.4  51.7  5.4  12.3  51.7  5.6  12.2
##         RLTBW
## P101042  44.8
## P101076  46.4
## P101095  61.5
## P101084  53.4
## P101038  52.0
## P101071  51.7
colnames(zeybel_metadata)
##  [1] "Stage"              "Metabolomics"       "Proteomics"         "GutMetagenomics"    "OralMetagenomics"  
##  [6] "LiverFatClass"      "Gender"             "AlcoholConsumption" "Smoker"             "Age"               
## [11] "LF"                 "Sodium"             "Potassium"          "Creatinine"         "Urea"              
## [16] "UA"                 "ALT"                "AST"                "GGT"                "AP"                
## [21] "TB"                 "Albumin"            "CK"                 "TC"                 "HDL"               
## [26] "LDL"                "Triglycerides"      "Glucose"            "Insulin"            "Weight"            
## [31] "BMI"                "HR"                 "SBP"                "DBP"                "WC"                
## [36] "HC"                 "TFM"                "TFFM"               "TTBW"               "LAFM"              
## [41] "LAFFM"              "LATBW"              "RAFM"               "RAFFM"              "RATBW"             
## [46] "LLFM"               "LLFFM"              "LLTBW"              "RLFM"               "RLFFM"             
## [51] "RLTBW"
  • otu table profile in Zeybel_Gut object
zeybel_otu <- otu_table(Zeybel_Gut_genus) %>% 
  data.frame()

head(zeybel_otu)
##                      P101042   P101076  P101095   P101084   P101038   P101071   P101047   P101012  P101027   P101024   P101057
## g__Absiella        0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000
## g__Acidaminococcus 0.0000000 0.0012421 0.00e+00 0.0114403 0.0000000 0.0017583 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000
## g__Actinomyces     0.0000893 0.0003643 2.79e-05 0.0000000 0.0000000 0.0000926 0.0002638 0.0004858 0.00e+00 0.0000555 0.0006398
## g__Adlercreutzia   0.0000000 0.0000000 0.00e+00 0.0002282 0.0000137 0.0000000 0.0000792 0.0003161 4.06e-05 0.0001708 0.0000000
## g__Agathobaculum   0.0004688 0.0000000 1.84e-04 0.0006843 0.0001106 0.0000364 0.0006443 0.0000074 9.73e-05 0.0028784 0.0002393
## g__Aggregatibacter 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000
##                      P101067   P101094   P101007   P101054   P101031   P101003   P101018   P101025  P101010   P101069   P101077
## g__Absiella        0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0002135 0.00e+00 0.0000000 0.0000000
## g__Acidaminococcus 0.0132178 0.0000000 0.0159577 0.0012000 0.0000000 0.0000000 0.0000000 0.0009027 0.00e+00 0.0001482 0.0000000
## g__Actinomyces     0.0002071 0.0000000 0.0002216 0.0000024 0.0313157 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000401 0.0002101
## g__Adlercreutzia   0.0000000 0.0001777 0.0000000 0.0000245 0.0000000 0.0000000 0.0008641 0.0000000 0.00e+00 0.0000158 0.0001580
## g__Agathobaculum   0.0000000 0.0020933 0.0001628 0.0007184 0.0000000 0.0002355 0.0000000 0.0061141 1.29e-05 0.0002268 0.0007352
## g__Aggregatibacter 0.0000000 0.0000000 0.0000000 0.0000000 0.0152900 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000
##                      P101065   P101096   P101085   P101021  P101052   P101030   P101041   P101078   P101051  P101068  P101056
## g__Absiella        0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.00e+00
## g__Acidaminococcus 0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000000 0.0002144 0.0000000 0.0000000 0.00e+00 0.00e+00
## g__Actinomyces     0.0001259 0.0000000 0.0000000 0.0000000 1.44e-05 0.0000000 0.0000000 0.0000940 0.0000000 1.99e-05 0.00e+00
## g__Adlercreutzia   0.0000000 0.0004234 0.0000000 0.0000000 0.00e+00 0.0003256 0.0000000 0.0000802 0.0000000 0.00e+00 0.00e+00
## g__Agathobaculum   0.0004899 0.0007549 0.0020973 0.0001715 0.00e+00 0.0021537 0.0000000 0.0006245 0.0001612 2.74e-05 2.83e-05
## g__Aggregatibacter 0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.0000000 0.0000000 0.0000000 0.0000000 0.00e+00 0.00e+00
##                      P101079   P101022   P101074   P101059 P101050   P101088   P101061   P101082   P101064
## g__Absiella        0.0000000 0.0000000 0.0000000 0.0000000       0 0.0000000 0.0000000 0.0000000 0.0000000
## g__Acidaminococcus 0.0000000 0.0000000 0.0141640 0.0029344       0 0.0000000 0.0000000 0.0030215 0.0000000
## g__Actinomyces     0.0000000 0.0001212 0.0000113 0.0000000       0 0.0000000 0.0000299 0.0000174 0.0000093
## g__Adlercreutzia   0.0000000 0.0000000 0.0004728 0.0000379       0 0.0002421 0.0000383 0.0000397 0.0000200
## g__Agathobaculum   0.0008838 0.0005000 0.0000000 0.0025444       0 0.0022848 0.0005402 0.0013902 0.0011816
## g__Aggregatibacter 0.0000000 0.0000000 0.0000000 0.0000000       0 0.0000000 0.0000000 0.0000000 0.0000000

Results:

  1. metadata has 42 continuous variables

12.3 Spearman Correlation Analysis

To identify the association between individual genus and continuous variables, we perform the correlation analysis with “spearman”, “pearson” and “kendall” method to calculate the test results. Here, the results have four statistical indexes: statistical, Rho, Pvalue and AdjustedPvalue and we also provide the plot_correlation_heatmap to display the results.

  • Calculation
dat_cor <- run_cor(ps = Zeybel_Gut_genus,
    columns = c("LF", "Sodium", "Potassium", "Creatinine", "Urea", "RLTBW"),
    method = "spearman")

head(dat_cor)
##   Phenotype             TaxaID Statistic         Rho    Pvalue AdjustedPvalue
## 1        LF        g__Absiella  12102.49  0.01932697 0.9033074      0.9808251
## 2        LF g__Acidaminococcus  11509.50  0.06737739 0.6715949      0.9297863
## 3        LF     g__Actinomyces  11349.08  0.08037634 0.6128579      0.9297863
## 4        LF   g__Adlercreutzia  11028.15  0.10638089 0.5025215      0.9297863
## 5        LF   g__Agathobaculum  13752.81 -0.11440021 0.4706651      0.9297863
## 6        LF g__Aggregatibacter  13533.57 -0.09663486 0.5426616      0.9297863
  • visualization
plot_correlation_heatmap(
      data = dat_cor,
      x_index = "Rho",
      x_index_cutoff = 0,
      y_index = "Pvalue",
      y_index_cutoff = 0.05,
      cellwidth = 35,
      cellheight = 10,
      fontsize_number = 15)
Spearman Correlation Coefficient

Figure 12.1: Spearman Correlation Coefficient

Results:

  1. the color of cell shows the size of Rho.
  • red: positive

  • blue: negative

  1. the asterisk of cell shows the significance:
  • * for [0.05, 0.01]

  • ** for less than 0.01

12.4 Partial Correlation Analysis

To identify the association between individual genus and continuous variables, while controlling third variables, we perform the partial correlation analysis with “spearman”, “pearson” and “kendall” method to calculate the test results. Here, the results have four statistical indexes: statistical, Rho, Pvalue and AdjustedPvalue and we also provide the plot_correlation_heatmap to display the results.

When comparing the Spearman Correlation Analysis, Partial Correlation Analysis has adjusted effects from the confounding factors as third variables. For instance, we should pay attention to the age or gender etc, which could affect the test results when we do some association analysis.

  • Calculation
dat_cor_partial <- run_partial_cor(ps = Zeybel_Gut_genus,
    columns = c("LF", "Sodium", "Potassium", "Creatinine", "Urea", "RLTBW"),
    AdjVars = c("Age", "Gender", "Smoker"),
    method_t = "pcor",
    method = "spearman",
    p_adjust = "BH")

head(dat_cor_partial)
##   Phenotype             TaxaID   Statistic         Rho    Pvalue AdjustedPvalue
## 1        LF        g__Absiella  0.07849237  0.01290299 0.9378594      0.9942613
## 2        LF g__Acidaminococcus  0.41767935  0.06850475 0.6785956      0.9738542
## 3        LF     g__Actinomyces  0.53412751  0.08747343 0.5964482      0.9738542
## 4        LF   g__Adlercreutzia  0.54397242  0.08907305 0.5897236      0.9738542
## 5        LF   g__Agathobaculum -0.43073071 -0.07063482 0.6691631      0.9738542
## 6        LF g__Aggregatibacter -0.34019586 -0.05584059 0.7356329      0.9738542
  • visualization
plot_correlation_heatmap(
      data = dat_cor_partial,
      x_index = "Rho",
      x_index_cutoff = 0,
      y_index = "Pvalue",
      y_index_cutoff = 0.05,
      cellwidth = 35,
      cellheight = 10,
      fontsize_number = 15)
Partial Correlation Coefficient

Figure 12.2: Partial Correlation Coefficient

Results:

  1. the color of cell shows the size of Rho.
  • red: positive

  • blue: negative

  1. the asterisk of cell shows the significance:
  • * for [0.05, 0.01]

  • ** for less than 0.01

12.5 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     2022-10-08
##  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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References

Zeybel, Mujdat, Muhammad Arif, Xiangyu Li, Ozlem Altay, Hong Yang, Mengnan Shi, Murat Akyildiz, et al. 2022. “Multiomics Analysis Reveals the Impact of Microbiota on Host Metabolism in Hepatic Steatosis.” Advanced Science 9 (11): 2104373.