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 packages required for the analyses described here.

#To load MetaHD package
library(MetaHD)


#If not installed already, first install the following packages
#install.packages(c("ggplot2","corpcor", "dplyr", "MASS", "Matrix", "matrixcalc", "metafor", "tidyr"))

library(ggplot2)
library(corpcor)
library(dplyr)
library(MASS)
library(Matrix)
library(matrixcalc)
library(metafor)
library(tidyr)

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

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
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
GCLC_data <- realdata$all

head(GCLC_data)

## # A tibble: 6 × 16
##   Study Group   met1   met2   met3  met4   met5   met6  met7   met8  met9  met10
##   <fct> <fct>  <dbl>  <dbl>  <dbl> <dbl>  <dbl>  <dbl> <dbl>  <dbl> <dbl>  <dbl>
## 1 GC    GroupA  946. 312766 2.49e3 14786 515534  89032 43965  3715  36790 3.38e5
## 2 GC    GroupA 4717  351637 1.14e3 19534 849373 146551 57531  4165  47936 5.11e5
## 3 GC    GroupA  792. 378899 2.56e5 22832 979930 141221 65416  4342  54750 5.80e5
## 4 GC    GroupB 1696. 495334 1.41e5 18590 454416 177805 53282  2120. 31380 2.71e5
## 5 GC    GroupB 3842  511079 2.01e5 20856 607470 265593 70361 13680  41824 2.30e5
## 6 GC    GroupB  174. 320974 1.01e5 14159 414228 118339 37396  1645. 26373 3.13e5
## # ℹ 4 more variables: met11 <dbl>, met12 <dbl>, met13 <dbl>, met14 <dbl>

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(GCLC_data)

#To obtain effect sizes of the two studies 
Y <- input_data$Y
Y

##           met1      met2         met3        met4        met5       met6
## GC -0.12216161 0.2408217  0.532996014 -0.06407204 -0.46280293 0.39930861
## LC  0.08730353 0.1074026 -0.002691788 -0.06451353 -0.02644625 0.08896313
##           met7       met8        met9      met10     met11     met12     met13
## GC -0.03582016  0.3558101 -0.33696133 -0.5623149 0.3591404 0.6540438 0.5257092
## LC  0.08684274 -0.1067551 -0.07022999  0.6563386 0.8378621 0.4823036 0.6567706
##         met14
## GC -0.6275364
## LC  0.5955336

#To obtain covariance matrices of the two studies
Slist <- input_data$Slist
head(Slist[[1]])

##             met1          met2        met3        met4          met5
## met1  0.66816536  0.0342995006 -0.13023460 0.042994441  0.0410169309
## met2  0.03429950  0.0219976435  0.04471369 0.007700712 -0.0003259779
## met3 -0.13023460  0.0447136868  0.99714813 0.028561626 -0.0153806902
## met4  0.04299444  0.0077007122  0.02856163 0.027139572  0.0142878276
## met5  0.04101693 -0.0003259779 -0.01538069 0.014287828  0.0456121062
## met6  0.06753777  0.0191195280  0.07398638 0.015472822  0.0042490167
##             met6       met7       met8         met9        met10       met11
## met1 0.067537772 0.06644299 0.19414892  0.042673455 -0.001689788 0.047069456
## met2 0.019119528 0.01098242 0.02419960  0.001167182 -0.006991485 0.022944009
## met3 0.073986377 0.02910402 0.02191034 -0.013459772 -0.029697299 0.142481398
## met4 0.015472822 0.01730561 0.03445267  0.012254275  0.005002414 0.022998932
## met5 0.004249017 0.01644365 0.03498665  0.019199078  0.015381516 0.009998191
## met6 0.073516461 0.02205751 0.05878542  0.005020851 -0.010286200 0.044605360
##              met12        met13      met14
## met1 -0.0200050944  0.005789995 0.07014588
## met2  0.0075920184  0.011643333 0.01405622
## met3  0.0777879265  0.080134515 0.08293142
## met4 -0.0003712348  0.006499256 0.03996221
## met5 -0.0099810545 -0.003704279 0.04585254
## met6  0.0151338588  0.021938095 0.01626860

head(Slist[[2]])

##               met1        met2        met3          met4         met5
## met1  9.760340e-04 0.001812946 0.001196608 -3.123434e-05 0.0004706615
## met2  1.812946e-03 0.011895588 0.007145803  2.365619e-03 0.0039408463
## met3  1.196608e-03 0.007145803 0.012252935  3.726777e-03 0.0050094004
## met4 -3.123434e-05 0.002365619 0.003726777  4.317303e-03 0.0030995727
## met5  4.706615e-04 0.003940846 0.005009400  3.099573e-03 0.0056326964
## met6  1.807128e-03 0.007574407 0.007296970  2.777669e-03 0.0043877345
##             met6        met7          met8          met9       met10
## met1 0.001807128 0.001676030 -0.0003892004 -4.995402e-05 0.009328585
## met2 0.007574407 0.007038615  0.0008048589  2.285953e-03 0.033840530
## met3 0.007296970 0.006681977  0.0021742618  3.692210e-03 0.029804410
## met4 0.002777669 0.002484183  0.0020911477  2.766641e-03 0.008500386
## met5 0.004387734 0.003950886  0.0019668889  2.973791e-03 0.016885650
## met6 0.012068748 0.007401765  0.0010282640  2.644378e-03 0.033995340
##              met11       met12         met13       met14
## met1  0.0038770580 0.006042901  0.0016242217 0.004513836
## met2  0.0106508074 0.021850123  0.0038966834 0.014419483
## met3  0.0057550080 0.018648155  0.0014382841 0.010107237
## met4 -0.0008921785 0.005920697 -0.0007960355 0.001814299
## met5  0.0019458596 0.011246777  0.0003337746 0.005426655
## met6  0.0098994407 0.022358561  0.0036476832 0.014163491