Chapter 2 Preliminery settings

2.1 Installing the package

MetaHD can be download via CRAN as follows:

#Using CRAN
install.packages("MetaHD")
library(MetaHD)

Alternatively, the development version can be downloaded using GitHub. To install this version, the user needs to make sure that Rtools has been installed and integrated prior.

# install.packages("devtools")
library(devtools)
devtools::install_github("metaanalysisR/MetaHD")

Along with MetaHD, we will also load the following package to run the analyses described here.

#To load MetaHD package
library(MetaHD)


#If not installed already, first install the following package
#install.packages(c("ggplot2"))

library(ggplot2)

2.2 Preparing the data

There are two ways of preparing the data.

2.2.1 Using summary estimates of the data

To carry out meta-analysis using MetaHD, the user needs to have

  1. observed effect sizes of the outcomes in the form of a \(K \times N\) matrix, where \(K\) is the number of studies and \(N\) is the number of metabolites, and
  2. a \(K\)-dimensional list of \(N \times N\) matrices representing within-study covariances in each study. If within-study correlations are not available, the variances can be entered in the form of a \(K \times N\) matrix.

The user can enter these summary estimates directly in the aforementioned format.

2.2.2 Using individual-level data

When individual data are available, the user can use `MetaHDInput’ function in the package to obtain the summary estimates in the above format. To do this, individual data must be in the following data frame format with study and group names as factors in the first and second columns respectively. To demostrate the required format, we have prepared the following dataset (described in section 3.1 in detail) as an R object within the MetaHD package and can be loaded using the following command.

# Read the data
example_data <- realdata
head(example_data)
##    Study  Group  met1  met2 met3  met4   met5  met6   met7  met8   met9  met10
## 1 study1 Group2  6799  7786 2094  5015  94588 29059  59943 10766  57238  53146
## 2 study1 Group2 17473 15277 1229  2336 183357 42891  87805  9667 132990  76963
## 3 study1 Group2 38267  7794 1021  1509 189323 54100  92716 11704  75001 129903
## 4 study1 Group2 12027  9810 2840  4757 158568 61752  67235 13161  97882  88858
## 5 study1 Group2 19565 12725 1946  6757 109405 53814 105437 10540  91196 108946
## 6 study1 Group2 20174 18446 3155 13223 125119 51873  78343 12801 123272  96326
##   met11 met12 met13 met14
## 1 18014 12783 44527 24969
## 2 34100 19631  8522 80978
## 3 25723 22843  7347 94906
## 4 33773 19223 13057 94041
## 5 27941 35500 14638 92369
## 6 35185 28896 13847 82009

Using the above input data format, `MetaHDInput’ function can then calculate the log Ratio of Means (ROM) effect size measures and their within-study covariance matrices as shown below.

input_data <- MetaHDInput(example_data)

#To obtain effect sizes of the two studies 
Y <- input_data$Y
Y
##               met1      met2       met3      met4        met5         met6
## study1 -0.08509765 0.0693374 0.21205246 0.1555140 -0.07131115 -0.010729262
## study2 -0.19103975 0.1985993 0.05266387 0.3042082 -0.08507508 -0.002200277
##               met7        met8        met9      met10      met11       met12
## study1 -0.06351313  0.07239534 -0.02854481 -0.1874413 0.07670815 -0.04170964
## study2 -0.02427915 -0.08497286 -0.10315078 -0.1196099 0.07105905  0.21213759
##            met13       met14
## study1 0.3378766 -0.20944830
## study2 0.2136121 -0.03345043
#To obtain covariance matrices of the two studies
Slist <- input_data$Slist
head(Slist[[1]])
##               met1         met2          met3         met4         met5
## met1  0.0194438538 0.0005017922 -0.0025219163 -0.002766591  0.004992537
## met2  0.0005017922 0.0161291933  0.0042058671  0.019843118  0.002535913
## met3 -0.0025219163 0.0042058671  0.0078052190  0.007030307 -0.001285968
## met4 -0.0027665912 0.0198431182  0.0070303073  0.074406890 -0.003247293
## met5  0.0049925375 0.0025359133 -0.0012859676 -0.003247293  0.007725915
## met6  0.0036952818 0.0035899796  0.0007582538  0.004244171  0.003155352
##              met6          met7          met8        met9        met10
## met1 0.0036952818  0.0036649961  0.0014738763 0.002466527  0.006057970
## met2 0.0035899796  0.0023004763  0.0009291760 0.003908964  0.001622693
## met3 0.0007582538 -0.0002967142  0.0010746462 0.001839553 -0.002939468
## met4 0.0042441714  0.0033677211  0.0053704235 0.004464203 -0.001121570
## met5 0.0031553517  0.0026034856 -0.0006582206 0.002925312  0.005845677
## met6 0.0035383608  0.0025416742  0.0003542152 0.003045991  0.003098343
##            met11        met12        met13        met14
## met1 0.003742686 0.0058389366 -0.017656900  0.006189749
## met2 0.006128897 0.0057661483 -0.015584915  0.002615291
## met3 0.002551313 0.0003375944  0.001663327 -0.001850528
## met4 0.005545238 0.0033374745 -0.012707585  0.001019416
## met5 0.004305489 0.0060072612 -0.020351263  0.004988612
## met6 0.003080440 0.0039539767 -0.012411644  0.003099160
head(Slist[[2]])
##               met1          met2          met3          met4          met5
## met1  0.0109767710 -0.0033067379 -0.0012616785 -0.0083336759  0.0003922567
## met2 -0.0033067379  0.0146428884  0.0037077689  0.0159669413 -0.0003152230
## met3 -0.0012616785  0.0037077689  0.0056022748  0.0053436283  0.0002016495
## met4 -0.0083336759  0.0159669413  0.0053436283  0.0622328293 -0.0055739904
## met5  0.0003922567 -0.0003152230  0.0002016495 -0.0055739904  0.0058255205
## met6 -0.0001745795 -0.0003574126  0.0001652289  0.0001864308 -0.0005042725
##               met6          met7          met8          met9         met10
## met1 -0.0001745795  0.0006382287  7.832299e-04  0.0003470474  0.0020580008
## met2 -0.0003574126 -0.0007568195  1.738323e-04  0.0007167777 -0.0013939525
## met3  0.0001652289  0.0005244583  1.013833e-03  0.0012836425 -0.0020003745
## met4  0.0001864308  0.0022826581  3.107372e-03 -0.0013166970 -0.0049094301
## met5 -0.0005042725 -0.0001647809  6.540804e-04  0.0002988824  0.0019182789
## met6  0.0005793504  0.0003004147 -4.307803e-05 -0.0002222715 -0.0002523751
##              met11         met12         met13         met14
## met1 -0.0010302778  0.0003585147 -9.433404e-05  0.0014432093
## met2  0.0022314227  0.0008032427 -2.013680e-04 -0.0001315375
## met3  0.0036313545  0.0013803512 -6.784827e-04 -0.0009400033
## met4  0.0023315513  0.0002988431 -3.343779e-03 -0.0017646934
## met5  0.0014421682  0.0004770191 -2.735012e-03  0.0013558693
## met6 -0.0001936851 -0.0002078362  1.382371e-04 -0.0001396999