Examples

Load metabolites and OTU abundance data of plant.² The OTU had been binned into genera level and were save as the metabolites_and_genera.rda file

path <- system.file('extdata', 'metabolites_and_genera.rda', package = 'mbOmic')

load(path)

Construct `mbSet` object.

bSet is S4 class containing the metabolites abundance matrix.

We can use bSet function to directly create bSet class.

names(metabolites)[1] <- 'rn'
m <- mSet(m = metabolites)
m
#> 1. Features( 12 ): 
#>   Delavirdine Leucoside 山奈酚3-O-桑布双糖苷 (3S)-2'-(Methylsulfanyl)-4'H-spiro[indole-3,5'-[1,3]thiazol]-2(1H)-one Cefepime versicotide A ...
#> 2. Samples( 247 ): 
#>   BS1 BS2 BS3 BS4 BS5 ...
#> 3. Top 5 Samples data:
#> [1] 1 2 3 4 5

There are some function to get or set information of a bSet, such as samples and features.

Extract the samples names from bSet class by function Samples.

samples(m)
#>  [1] "BS1" "BS2" "BS3" "BS4" "BS5" "BS6" "SS1" "SS2" "SS3" "SS4" "SS5" "SS6"
#[1] "BS1" "BS2" "BS3" "BS4" "BS5" "BS6" "SS1" "SS2" "SS3" "SS4" "SS5" "SS6"

Remove bad analytes (OTU and metatoblites)

Removal of analytes only measured in <2 of samples can perform by clean_analytes.

m <- clean_analytes(m, fea_num = 2)

Generate metabolite module

mbOmic can generate metabolite module by coExpress function. The coExpress function is the encapsulation of one-step network construction and module detection of WGCNA package. The coExpress function firstly pick up the soft-threshold. The threshold.d and threshold parameters are used to detect whether is \(R^2\) changing and appropriate.

If there are no appropriate threshold was detected and you do not set the power parameter, the coExpress will throw a error, “No power detected! pls set the power parameter”.

net <- try({
  coExpress(m,message = TRUE,threshold.d = 0.02, threshold = 0.8, plot = TRUE) 
})

#> Error in coExpress(m, message = TRUE, threshold.d = 0.02, threshold = 0.8,  : 
#>   No power detected! pls set the power parameter
class(net)
#> [1] "try-error"

If you can’t get a good scale-free topology index no matter how high set the soft-thresholding power, you can directly set the power value by the parameter power, but should be looked into carefully. The appropriate soft-thresholding power can be chosen based on the number of samples as in the table below (recommend by WGCNA package).

Number of samples	Unsigned and signed hybrid networks	Signed networks
<20	9	18
20~30	8	16
30~40	7	14
>40	6	12

net <- coExpress(m,message = TRUE,threshold.d = 0.02, threshold = 0.8, power = 9)

Calculate the Spearman metabolite-genera correlation

you can calculate the correlation between metabolites and OTUs by corr function. It return a data table containing rho, p value, and adjust p value. Moreover, the corr can run in parallel mode.

b <- genera
names(b)[1] <- 'rn'
b <- bSet(b=b)
spearm <- corr(m = m, b = b, method = 'spearman')
# head(spearm)
spearm[p<=0.001]
#>                                b                                       m
#>   1: unidentified_Acidimicrobiia                             Delavirdine
#>   2:                 Acidibacter          Leucoside 山奈酚3-O-桑布双糖苷
#>   3:                      Dongia          Leucoside 山奈酚3-O-桑布双糖苷
#>   4:  unidentified_Clostridiales          Leucoside 山奈酚3-O-桑布双糖苷
#>   5:                 Acidibacter                           versicotide A
#>  ---                                                                    
#> 366: unidentified_Acidimicrobiia                        Methyl cinnamate
#> 367:                Chujaibacter                               Catharine
#> 368:                   Ralstonia                               Catharine
#> 369:  unidentified_Clostridiales                               Catharine
#> 370:      Candidatus_Udaeobacter Tri-2,5-cyclohexadien-1-yl(octyl)silane
#>             rho            p        padj
#>   1:  0.8391608 0.0006428260 0.010134767
#>   2: -0.8321678 0.0007854417 0.010744842
#>   3:  0.8601399 0.0003316683 0.008057909
#>   4: -0.8391608 0.0006428260 0.010134767
#>   5: -0.8881119 0.0001141336 0.006342975
#>  ---                                    
#> 366:  0.8671329 0.0002598118 0.007310908
#> 367: -0.8321678 0.0007854417 0.010744842
#> 368: -0.8861660 0.0001239889 0.006526816
#> 369: -0.8881119 0.0001141336 0.006342975
#> 370:  0.8531469 0.0004181179 0.008646290

plot the network

Finally, you can vaisulize the network by plot_network function, taking the coExpressand corr output. The orange nodes correspondes to OTU(genera)).

# svg('../inst/doc/network1.svg')
plot_network(net, spearm[abs(rho) >= 0.8 & p <= 0.001], interaction = FALSE)

# dev.off()

plot_network(net, spearm[abs(rho) >= 0.8 & p <= 0.001], seed = 123, interaction = TRUE, return =  TRUE)

# visSave(tmp, file = '../inst/doc/network2.html')

SessionInfo

devtools::session_info()
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McHardy, I. H., Goudarzi, M., Tong, M., Ruegger, P. M., Schwager, E., Weger, J. R., Graeber, T. G., Sonnenburg, J. L., Horvath, S., Huttenhower, C., McGovern, D. P., Fornace, A. J., Borneman, J., & Braun, J. (2013). Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome, 1(1), 17. https://doi.org/10.1186/2049-2618-1-17 ↩
Huang, W., Sun, D., Chen, L., & An, Y. (2021). Integrative analysis of the microbiome and metabolome in understanding the causes of sugarcane bitterness. Scientific Reports, 11(1), 1-11.↩

Integrative analysis of metabolome and microbiome

Congcong Gong

Introduction

Examples

Construct `mbSet` object.

Remove bad analytes (OTU and metatoblites)

Generate metabolite module

Calculate the Spearman metabolite-genera correlation

plot the network

SessionInfo

Integrative analysis of metabolome and microbiome

Congcong Gong

Introduction

Examples

Construct mbSet object.

Remove bad analytes (OTU and metatoblites)

Generate metabolite module

Calculate the Spearman metabolite-genera correlation

plot the network

SessionInfo

Construct `mbSet` object.