Loading Exposome Data
From TXT files
The function readExposome will create an ExposomeSet from the three txt files. The following lines are used to locate these three files, that were included in the package for demonstration purposes.
path <- file.path(path.package("rexposome"), "extdata")
description <- file.path(path, "description.csv")
phenotype <- file.path(path, "phenotypes.csv")
exposures <- file.path(path, "exposures.csv")
These files follows the rules described in Data Format section. They are csv files, meaning each values is split from the others by a comma (,). Function readExposome allows to load most any type of files containing exposome data:
args(readExposome)
## function (exposures, description, phenotype, sep = ",", na.strings = c("NA",
## "-", "?", " ", ""), exposures.samCol = "sample", description.expCol = "exposure",
## description.famCol = "family", phenotype.samCol = "sample",
## exposures.asFactor = 5, warnings = TRUE)
## NULL
readExposome expects, by default, csv files. Changing the content of the argument sep will allow to load other files types. The missing values are set using the argument na.strings.
This means that the character assigned to this argument will be
interpreted as a missing value. By default, those characters are "NA", "-", "?", " " and "". Then, the columns with the exposures’ names and the individual’s names need to be indicated. Arguments exposures.samCol and phenotype.samCol indicates the column with the individuals’ names at exposures file and phenotypes file. The arguments description.expCol and description.famCol indicates the column containing the exposures’ names and the exposures’ family in the description file.
exp <- readExposome(
exposures = exposures,
description = description,
phenotype = phenotype,
exposures.samCol = "idnum",
description.expCol = "Exposure",
description.famCol = "Family",
phenotype.samCol = "idnum"
)
The result is an object of class ExposomeSet, that can show all the information of the loaded exposome:
exp
## Object of class 'ExposomeSet' (storageMode: environment)
## . exposures description:
## . categorical: 4
## . continuous: 84
## . exposures transformation:
## . categorical: 0
## . transformed: 0
## . standardized: 0
## . imputed: 0
## . assayData: 88 exposures 109 individuals
## . element names: exp
## . exposures: AbsPM25, ..., X7OHMMeOP
## . individuals: id001, ..., id108
## . phenoData: 109 individuals 9 phenotypes
## . individuals: id001, ..., id108
## . phenotypes: whistling_chest, ..., cbmi
## . featureData: 88 exposures 7 explanations
## . exposures: AbsPM25, ..., X7OHMMeOP
## . descriptions: Family, ..., .imp
## experimentData: use 'experimentData(object)'
## Annotation:
Under the section exposures description the number of continuous (84) and categorical (4) exposures are shown. The assayData, phenoData and featureData shows the content of the files we loaded with readExposome.
From data.frame
The function loadExposome allows to create an ExposomeSet through three data.frames: one as description data, one as exposures data and one as phenotypes data. The arguments are similar to the ones from readExposome:
args(loadExposome)
## function (exposures, description, phenotype, description.famCol = "family",
## exposures.asFactor = 5, warnings = TRUE)
## NULL
In order to illustrate how to use loadExposome, we are loading the previous csv files as data.frames:
dd <- read.csv(description, header=TRUE)
ee <- read.csv(exposures, header=TRUE)
pp <- read.csv(phenotype, header=TRUE)
Then we rearrange the data.frames to fulfil with the requirements of the exposome data. The data.frame corresponding to description data needs to have the exposure’s names as rownames.
rownames(dd) <- dd[, 2]
dd <- dd[ , -2]
The data.frame corresponding to exposures data needs to have the individual’s identifiers as rownames:
rownames(ee) <- ee[ , 1]
ee <- ee[ , -1]
The data.frame corresponding to phenotypes data needs to have the individual’s identifiers as a rownames, as the previous data.frame:
rownames(pp) <- pp[ , 1]
pp <- pp[ , -1]
Then, the ExposomeSet is creating by giving the three data.frames to loadExposome:
exp <- loadExposome(
exposures = ee,
description = dd,
phenotype = pp,
description.famCol = "Family"
)
Accessing to Exposome Data
The class ExposomeSet has several accessors to get the data stored in it. There are four basic methods that returns the names of the individuals (sampleNames), the name of the exposures (exposureNames), the name of the families of exposures (familyNames) and the name of the phenotypes (phenotypeNames).
head(sampleNames(exp))
## [1] "id001" "id002" "id003" "id004" "id005" "id006"
head(exposureNames(exp))
## [1] "AbsPM25" "As" "BDE100" "BDE138" "BDE153" "BDE154"
familyNames(exp)
## [1] "Air Pollutants" "Metals" "PBDEs"
## [4] "Organochlorines" "Bisphenol A" "Water Pollutants"
## [7] "Built Environment" "Cotinine" "Home Environment"
## [10] "Phthalates" "Noise" "PFOAs"
## [13] "Temperature"
phenotypeNames(exp)
## [1] "whistling_chest" "flu" "rhinitis" "wheezing"
## [5] "birthdate" "sex" "age" "cbmi"
## [9] "blood_pre"
fData will return the description of the exposures (including internal information to manage them).
head(fData(exp), n = 3)
## Family Name .fct .trn
## AbsPM25 Air Pollutants Measurement of the blackness of PM2.5 filters
## As Metals Asenic
## BDE100 PBDEs Polybrominated diphenyl ether -100
## .std .imp .type
## AbsPM25 numeric
## As numeric
## BDE100 numeric
pData will return the phenotypes information.
head(pData(exp), n = 3)
## whistling_chest flu rhinitis wheezing birthdate sex age cbmi blood_pre
## id001 never no no no 2004-12-29 male 4.2 16.3 120
## id002 never no no no 2005-01-05 male 4.2 16.4 121
## id003 7-12 epi no no yes 2005-01-05 male 4.2 19.0 120
Finally, the method expos allows to obtain the matrix of exposures as a data.frame:
expos(exp)[1:10, c("Cotinine", "PM10V", "PM25", "X5cxMEPP")]
## Cotinine PM10V PM25 X5cxMEPP
## id001 0.03125173 0.10373078 1.176255 NA
## id002 1.59401990 -0.47768393 1.155122 NA
## id003 1.46251090 NA 1.215834 1.859045
## id004 0.89059991 NA 1.171610 NA
## id005 NA NA 1.145765 NA
## id006 0.34818304 NA 1.145382 NA
## id007 1.53591130 NA 1.174642 NA
## id008 2.26864700 NA 1.165078 1.291871
## id009 1.24842660 NA 1.171406 1.650948
## id010 -0.36758339 0.01593277 1.179240 2.112357
Exposome Pre-process
Missing Data in Exposures and Phenotypes
The number of missing data on each exposure and on each phenotype can be found by using the function tableMissings. This function returns a vector with the amount of missing data in each exposure or phenotype. The argument set indicates if the number of missing values is counted on exposures of phenotypes. The argument output indicates if it is shown as counts (output="n") or as percentage (output="p").
The current exposome data has no missing in the exposures nor in the phenotypes:
tableMissings(exp, set = "exposures", output = "n")
## Dens Temp Conn AbsPM25 NO NO2
## 0 0 1 2 2 2
## NOx PM10 PM10Cu PM10Fe PM10K PM10Ni
## 2 2 2 2 2 2
## PM10S PM10SI PM10Zn PM25 PM25CU PM25FE
## 2 2 2 2 2 2
## PM25K PM25Ni PM25S PM25Sl PM25Zn PMcoarse
## 2 2 2 2 2 2
## Benzene PM25V ETS G_pesticides Gas BTHM
## 3 3 5 5 5 6
## CHCl3 H_pesticides Noise_d Noise_n THM Cotinine
## 6 6 6 6 6 7
## bHCH DDE DDT HCB PCB118 PCB138
## 13 13 13 13 13 13
## PCB153 PCB180 BPA As Cs Mo
## 13 13 21 24 24 24
## Ni Tl Zn Hg Cd Sb
## 24 24 24 27 28 30
## Green Cu PM10V Se MBzP MEHHP
## 31 40 41 45 46 46
## MEHP MEOHP MEP MiBP MnBP X5cxMEPP
## 46 46 46 46 46 46
## Co PFHxS PFNA PFOA PFOS X7OHMMeOP
## 47 48 48 48 48 49
## Pb X2cxMMHP BDE100 BDE138 BDE153 BDE154
## 59 64 76 76 76 76
## BDE17 BDE183 BDE190 BDE209 BDE28 BDE47
## 76 76 76 76 76 76
## BDE66 BDE71 BDE85 BDE99
## 76 76 76 76
tableMissings(exp, set = "phenotypes", output = "n")
## whistling_chest flu rhinitis wheezing sex
## 0 0 0 0 0
## age cbmi blood_pre birthdate
## 0 0 2 3
Alternatively to tableMissings, the function plotMissings draw a bar plot with the percentage of missing data in each exposure of phenotype.
plotMissings(exp, set = "exposures")
Exposures Normality
Most of the test done in exposome analysis requires that the exposures must follow a normal distribution. The function normalityTest performs a test on each exposure for normality behaviour. The result is a data.frame with the exposures’ names, a flag TRUE/FALSE for normality and the p-value obtained from the Shapiro-Wilk Normality Test (if the p-value is under the threshold, then the exposure is not normal).
nm <- normalityTest(exp)
table(nm$normality)
##
## FALSE TRUE
## 55 29
So, the exposures that do not follow a normal distribution are:
nm$exposure[!nm$normality]
## [1] "DDT" "PM10SI" "PM25K" "PM25Sl" "PCB118" "Tl"
## [7] "PM10V" "PM25Zn" "PM25FE" "PM10K" "BDE17" "PM25"
## [13] "PMcoarse" "PM10" "BPA" "Green" "NO2" "Cs"
## [19] "PFNA" "PCB153" "PM25CU" "MEOHP" "Cu" "HCB"
## [25] "MEHHP" "DDE" "BDE190" "bHCH" "PM10Zn" "MnBP"
## [31] "NO" "NOx" "PM10S" "MEHP" "PCB138" "Zn"
## [37] "X2cxMMHP" "PCB180" "PFOA" "Cotinine" "PM25S" "Co"
## [43] "Conn" "PM25Ni" "PFHxS" "PM10Ni" "Cd" "Dens"
## [49] "Se" "X5cxMEPP" "BDE183" "BDE28" "Sb" "BDE138"
## [55] "PM25V"
Some of these exposures are categorical so they must not follow a normal distribution. This is the case, for example, of G_pesticides. If we plot the histogram of the values of the exposures it will make clear:
library(ggplot2)
plotHistogram(exp, select = "G_pesticides") +
ggtitle("Garden Pesticides")
Some others exposures are continuous variables that do not overpass
the normality test. A visual inspection is required in this case.
plotHistogram(exp, select = "BDE209") +
ggtitle("BDE209 - Histogram")
If the exposures were following an anon normal distribution, the method plotHistogram has an argument show.trans that set to TRUE draws the histogram of the exposure plus three typical transformations:
plotHistogram(exp, select = "BDE209", show.trans = TRUE)
Exposures Imputation
The imputation process is out of rexposome scope. Nevertheless, rexposome incorporates a wrapper to run the imputation tools from the R packages and Hmisc. The imputation of the exposures in the ExposomeSet is done by using this code:
exp <- imputation(exp)
To use mice package instead of hmisc, see the vignette entitles Dealing with Multiple Imputations.
Exposures Behaviour
We can get a snapshot of the behaviour of the full exposome using the method plotFamily or its wrapper plot. This function allows drawing a plot of a given family of exposures or a mosaic with all the exposures.
plotFamily(exp, family = "all")
This plotting method allows to group the exposure by a given phenotype using the argument group:
plotFamily(exp, family = "Phthalates", group = "sex")
The same method allows to include a second group using the argument group2:
plotFamily(exp, family = "Phthalates", group = "rhinitis", group2 = "rhinitis")
Exposures PCA
To properly perform a PCA analysis the exposures needs to be standardised. The standardisation is done using function standardize that allows using a normal and a robust approaches or use the interquartile range. The normal aproache scales the exposures using the mean as a centre and the standard variation used as dispersion. In robust aproach the median and the median absolute deviation are used. This transformation are only applied to continuous exposures. When interquartile range
is used, the median is used as a center and the coeficient between the
interquartile range of the exposure and the normal range between the
percentile 75 and 25 as variance.
exp_std <- standardize(exp, method = "normal")
exp_std
## Object of class 'ExposomeSet' (storageMode: environment)
## . exposures description:
## . categorical: 4
## . continuous: 84
## . exposures transformation:
## . categorical: 0
## . transformed: 0
## . standardized: 84
## . imputed: 88
## . assayData: 88 exposures 109 individuals
## . element names: exp
## . exposures: AbsPM25, ..., X7OHMMeOP
## . individuals: id001, ..., id108
## . phenoData: 109 individuals 9 phenotypes
## . individuals: id001, ..., id108
## . phenotypes: whistling_chest, ..., cbmi
## . featureData: 88 exposures 7 explanations
## . exposures: AbsPM25, ..., X7OHMMeOP
## . descriptions: Family, ..., .imp
## experimentData: use 'experimentData(object)'
## Annotation:
Once the exposures are standardized we can run a PCA on the ExposomeSet using the method pca.
exp_pca <- pca(exp_std)
The method pca returns an object of class ExposomePCA. This object encapsulates all the information generated by the principal component analysis. The method plotPCA can be used in several ways. The first way is setting the argument set to "all" to create a mosaic of plots.
plotPCA(exp_pca, set = "all")
The plots in the first row correspond to the exposures and samples
space. The first plot shows all the exposures on the axis for the first
and the second principal components. The second plot shows all the
individuals on the axis for the first and second principal components.
The plots on the second row are a summary of the variability
explained by each component. The first plot is a bar plot with the
variability explained by each component highlighting the components that
are being drawn in the two first plots. The second plot is a line plot
indicating the cumulative variability explained until each principal
component. The vertical dashed line indicates the last principal
component that is drawn in the first two plots. The horizontal dashed
line indicates the amount of explained variability.
A second way of using plotPCA is changing the content of the argument set to "samples" to see the samples’ space. When the set argument is filled with samples, the argument phenotype can be used to colour each sample with its phenotype value.
plotPCA(exp_pca, set = "samples", phenotype = "sex")
This plot shows the sample space of the first and the second
principal component. Each dot is a sample and it is coloured depending
on its value in sex. We can see that no cluster is done in terms of sex.
This view be recreated in a 3D space using the function plot3PCA:
plot3PCA(exp_pca, cmpX=1, cmpY=2, cmpZ=3, phenotype = "sex")
Exposures Correlation
The correlation between exposures, in terms of intra-family and
inter-family exposures, is interesting to take into account. The
correlation of the exposome can be computed using correlation.
exp_cr <- correlation(exp, use = "pairwise.complete.obs", method.cor = "pearson")
The values of the correlation can be obtained using the method extract. This returns a data.frame.
extract(exp_cr)[1:4, 1:4]
## AbsPM25 As BDE100 BDE138
## AbsPM25 1.00000000 -0.1681446 0.1098427 -0.03196037
## As -0.16814460 1.0000000 -0.3727296 0.13768481
## BDE100 0.10984268 -0.3727296 1.0000000 0.11482844
## BDE138 -0.03196037 0.1376848 0.1148284 1.00000000
The best option to see the inter-family correlations is the circos of correlations while the matrix of correlations is a better way for studying the intra-family correlations. Both of them are drawn using the method plotCorrelation.
plotCorrelation(exp_cr, type = "circos")
plotCorrelation(exp_cr, type = "matrix")
Individuals Clustering
Clustering analysis on exposures data results in exposure profile. The method clustering allows applying most of any clustering method to an ExposomeSet method.
The argument of the method clustering are:
args(clustering)
## function (object, method, cmethod, ..., warnings = TRUE)
## NULL
The argument method is filled with the clustering function. This clustering function needs to accept an argument called data, that will be filled with the exposures-matrix. The object obtained from the clustering function needs to have an accessor called classification. Otherwise the argument cmethod needs to be filled with a function that takes the results of the clustering function and returns a vector with the classification of each individual.
In this analysis we apply the clustering method hclust. Hence we create a function to accept an argument called data.
hclust_data <- function(data, ...) {
hclust(d = dist(x = data), ...)
}
The argument ... allows passing arguments from recposome’s clustering method to hclust.
Then, a function to obtain the classification of each sample is also required. This function will use the cutree function to obtain the labels.
hclust_k3 <- function(result) {
cutree(result, k = 3)
}
The new function hclust_k3 is a function that takes the results of hclust_data and applies it the cutree function, requesting 3 groups of individuals.
Having both clustering function (hclust_data) and the classification function (hclust_k3) we use them in the clustering method:
exp_c <- clustering(exp, method = hclust_data, cmethod = hclust_k3)
## Warning in clustering(exp, method = hclust_data, cmethod = hclust_k3): Non
## continuous exposures will be discarded.
exp_c
## Object of class 'ExposomeClust' (storageMode: environment)
## . Method: .... UTF-8
## . assayData: 84 exposures 109 samples
## . element names: exp
## . exposures: AbsPM25, ..., X7OHMMeOP
## . samples: id001, ..., id108
## . featureData: 84 exposures 7 explanations
## . exposures: AbsPM25, ..., X7OHMMeOP
## . descriptions: Family, ..., .imp
## . #Cluster: 3
The profile for each group of individuals can be plotted with plotClassification method.
plotClassification(exp_c)
The classification of each individual can be obtained using the method classification. We can get a table with the number of samples per group with:
table(classification(exp_c))
##
## 1 2 3
## 80 24 5
As seen, the groups are given as numbers and the plotClassification transforms it to names (Group 1, Group 2 and Group 3).
Exposure Association
Once preprocessed the exposome its association with health outcomes can be tested through three different approaches:
- Using the results of the PCA
- Testing the association of each exposure to a given trait (single exposure association)
- Testing the association of the exposome to a given trait (multiple exposure associations)
Understanding Principal Component Analysis
From the results of the PCA on the exposome data, two measures can be
obtained: the correlation of the exposures with the principal
components and the association of the phenotypes with the principal
components.
The method plotEXP draws a heat map with the correlation of each exposure to the principal components.
plotEXP(exp_pca) + theme(axis.text.y = element_text(size = 6.5)) + ylab("")
From the plot, some conclusions can be obtained:
- PC 1 is hight correlated with particle matter (PM) exposures.
- PC 2 is correlated with the Polybrominated Diphenyl Ethers (BDES) exposures.
- PC 5 is negative correlated with Perfluorooctanoic Acid (PFOAS) and Polychlorinated Biphenyl (PCBS).
These conclusions are useful to give a meaning to the Principal Components in terms of exposures.
The method plotPHE test the association between the
phenotypes and the principal components and draws a heat map with the
score of the association.
plotPHE(exp_pca)
The conclusions that can be taken from the heat map are:
- age is associated to PC 10.
- sex is associated to PC 6 and PC 10.
- flu is associated to PC 5.
Exposome-Wide Association Studies (ExWAS)
Method exwas performs univariate test of the association between exposures and health outcomes. This method requests a formula to test and the family of the distribution of the health outcome (dependent variable).
The following line performs an ExWAS on flu and wheezing adjusted by sex and age. Since the content of flu and others in the ExposomeSet is dichotomous, the family is set to binomial (for more information see ?glm).
fl_ew <- exwas(exp, formula = blood_pre~sex+age, family = "gaussian")
fl_ew
## An object of class 'ExWAS'
##
## blood_pre ~ sex+age
##
## Tested exposures: 88
## Threshold for effective tests (TEF): 1.28e-03
## . Tests < TEF: 7
we_ew <- exwas(exp, formula = wheezing~sex+age, family = "binomial")
we_ew
## An object of class 'ExWAS'
##
## wheezing ~ sex+age
##
## Tested exposures: 88
## Threshold for effective tests (TEF): 1.28e-03
## . Tests < TEF: 0
The method exwas calculates the effective number of tests in base of the correlation between the exposures. This is transformed into a threshold for the p-values of the association. This threshold can be obtained using the method tef.
A table with the associations between the exposures and flu is obtained with method extract:
head(extract(fl_ew))
## DataFrame with 6 rows and 4 columns
## pvalue effect X2.5 X97.5
## <numeric> <numeric> <numeric> <numeric>
## NOx 8.479912e-06 13.36163 7.775519 18.94775
## NO 1.115071e-05 10.92073 6.287551 15.55391
## NO2 1.160359e-05 15.40088 8.852820 21.94894
## AbsPM25 1.664364e-05 20.22802 11.454284 29.00176
## PM25 3.719198e-05 37.78052 20.606782 54.95426
## PM25CU 1.350290e-04 11.51706 5.825918 17.20819
A Manhattan-like plot with the p-values of the association between
each exposure and asthma, coloured by families of exposures, is draw by
method plotExwas.
clr <- rainbow(length(familyNames(exp)))
names(clr) <- familyNames(exp)
plotExwas(fl_ew, we_ew, color = clr) +
ggtitle("Exposome Association Study - Univariate Approach")
Then a plot for the effects of a given model can be obtained with plotEffect:
plotEffect(fl_ew)
Multivariate Exposome Analysis
The last approach is a multivariate analysis in order to find the
group of exposures related to the health outcome. This can be done using
methods like Elastic Net. The method mexwas applies elastic net to the exposures given a health outcome of interest.
bl_mew <- mexwas(exp_std, phenotype = "blood_pre", family = "gaussian")
we_mew <- mexwas(exp_std, phenotype = "wheezing", family = "binomial")
The coefficient of each exposure is plotted with plotExwas.
The method draws a heat map with two columns and the exposures as rows.
The heat map is coloured with the coefficient of each exposure in
relation with the health outcome, so the ones in white are not
interesting. The two columns of the heat map correspond to the minimum
lambda (Min) and to the lambda which gives the most regularised model such that error is within one standard error of the minimum (1SE).
plotExwas(bl_mew, we_mew) + ylab("") +
ggtitle("Exposome Association Study - Multivariate Approach")
