Title: Match Cases to Controls Based on Genotype Principal Components
Version: 0.3.3
Maintainer: Derek W. Brown <derek9@gwmail.gwu.edu>
Description: Matches cases to controls based on genotype principal components (PC). In order to produce better results, matches are based on the weighted distance of PCs where the weights are equal to the % variance explained by that PC. A weighted Mahalanobis distance metric (Kidd et al. (1987) <doi:10.1016/0031-3203(87)90066-5>) is used to determine matches.
License: MIT + file LICENSE
URL: https://github.com/machiela-lab/PCAmatchR
BugReports: https://github.com/machiela-lab/PCAmatchR/issues
Encoding: UTF-8
LazyData: true
Depends: R (≥ 3.5.0)
Suggests: optmatch, testthat, knitr, rmarkdown, R.rsp
VignetteBuilder: R.rsp
RoxygenNote: 7.2.2
NeedsCompilation: no
Packaged: 2022-11-30 12:24:40 UTC; myersta
Author: Derek W. Brown ORCID iD [aut, cre], Mitchel J. Machiela ORCID iD [aut], Timothy A. Myers ORCID iD [ctb], NCI [cph, fnd]
Repository: CRAN
Date/Publication: 2022-12-01 00:20:06 UTC

First 20 principal components of 2504 individuals from the 1000 Genome Project

Description

A sample dataset containing information about population, gender, and the first 20 principal components calculated from 2504 individuals in the Phase 3 data release of the 1000 Genomes Project. The principal component analysis was conducted using PLINK.

Usage

PCs_1000G

Format

A data frame with 2504 rows and 24 variables:

sample

sample ID number

pop

three letter designation of 1000 Genomes reference population

super_pop

three letter designation of 1000 Genomes reference super population

gender

gender of individual

PC1

principal component 1

PC2

principal component 2

PC3

principal component 3

PC4

principal component 4

PC5

principal component 5

PC6

principal component 6

PC7

principal component 7

PC8

principal component 8

PC9

principal component 9

PC10

principal component 10

PC11

principal component 11

PC12

principal component 12

PC13

principal component 13

PC14

principal component 14

PC15

principal component 15

PC16

principal component 16

PC17

principal component 17

PC18

principal component 18

PC19

principal component 19

PC20

principal component 20

Source

https://www.internationalgenome.org

Examples

head(PCs_1000G)
genome_PC <- PCs_1000G
# Create PCs
           PC <- as.data.frame(genome_PC[,c(1,5:24)])
           head(PC)

First 20 eigenvalues of 2504 individuals from the 1000 Genome Project

Description

A sample dataset containing the first 20 eigenvalues calculated from 2504 individuals in the Phase 3 data release of the 1000 Genomes Project. The principal component analysis was conducted using PLINK.

Usage

eigenvalues_1000G

Format

A data frame with 20 rows and 1 variable:

eigen_values

calculated eigenvalues

Source

Machiela Lab

Examples

  eigenvalues_1000G
genome_values <- eigenvalues_1000G
          values <- c(genome_values)$eigen_values

All eigenvalues of 2504 individuals from the 1000 Genome Project

Description

A sample dataset containing all the eigenvalues calculated from 2504 individuals in the Phase 3 data release of the 1000 Genomes Project. The principal component analysis was conducted using PLINK.

Usage

eigenvalues_all_1000G

Format

A data frame with 2504 rows and 1 variable:

eigen_values

calculated eigenvalues

Source

Machiela Lab

Examples

  eigenvalues_all_1000G
genome_values <- eigenvalues_all_1000G
          values <- c(genome_values)$eigen_values

Weighted matching of controls to cases using PCA results.

Description

Weighted matching of controls to cases using PCA results.

Usage

match_maker(
  PC = NULL,
  eigen_value = NULL,
  data = NULL,
  ids = NULL,
  case_control = NULL,
  num_controls = 1,
  num_PCs = NULL,
  eigen_sum = NULL,
  exact_match = NULL,
  weight_dist = TRUE,
  weights = NULL
)

Arguments

PC

Individual level principal component.

eigen_value

Computed eigenvalue for each PC. Used as the numerator to calculate the percent variance explained by each PC.

data

Dataframe containing id and case/control status. Optionally includes covariate data for exact matching.

ids

The unique id variable contained in both "PC" and "data."

case_control

The case control status variable.

num_controls

The number of controls to match to each case. Default is 1:1 matching.

num_PCs

The total number of PCs calculated within the PCA. Can be used as the denomiator to calculate the percent variance explained by each PC. Default is 1000.

eigen_sum

The sum of all possible eigenvalues within the PCA. Can be used as the denomiator to calculate the percent variance explained by each PC.

exact_match

Optional variables contained in the dataframe on which to perform exact matching (i.e. sex, race, etc.).

weight_dist

When set to true, matches are produced based on PC weighted Mahalanobis distance. Default is TRUE.

weights

Optional user defined weights used to compute the weighted Mahalanobis distance metric.

Value

A list of matches and weights.

Examples

# Create PC data frame by subsetting provided example dataset
pcs <- as.data.frame(PCs_1000G[,c(1,5:24)])
# Create eigenvalues vector using example dataset
eigen_vals <- c(eigenvalues_1000G)$eigen_values
# Create full eigenvalues vector using example dataset
all_eigen_vals<- c(eigenvalues_all_1000G)$eigen_values
# Create Covarite data frame
cov_data <- PCs_1000G[,c(1:4)]
# Generate a case status variable using ESN 1000 Genome population
cov_data$case <- ifelse(cov_data$pop=="ESN", c(1), c(0))
# With 1 to 1 matching
if(requireNamespace("optmatch", quietly = TRUE)){
                        library(optmatch)
                        match_maker(PC = pcs,
                                    eigen_value = eigen_vals,
                                    data = cov_data,
                                    ids = c("sample"),
                                    case_control = c("case"),
                                    num_controls = 1,
                                    eigen_sum = sum(all_eigen_vals),
                                    weight_dist=TRUE
                                   )
                        }

Function to plot matches from match_maker output

Description

Function to plot matches from match_maker output

Usage

plot_maker(
  data = NULL,
  x_var = NULL,
  y_var = NULL,
  case_control = NULL,
  line = T,
  ...
)

Arguments

data

match_maker output

x_var

Principal component 1

y_var

Principal component 2

case_control

Case or control status

line

draw line

...

Arguments passed to plot

Value

None

Examples


# run match_maker()
# Create PC data frame by subsetting provided example dataset
pcs <- as.data.frame(PCs_1000G[,c(1,5:24)])
# Create eigenvalues vector using example dataset
eigen_vals <- c(eigenvalues_1000G)$eigen_values
# Create full eigenvalues vector using example dataset
all_eigen_vals<- c(eigenvalues_all_1000G)$eigen_values
# Create Covarite data frame
cov_data <- PCs_1000G[,c(1:4)]
# Generate a case status variable using ESN 1000 Genome population
cov_data$case <- ifelse(cov_data$pop=="ESN", c(1), c(0))
# With 1 to 1 matching
 if(requireNamespace("optmatch", quietly = TRUE)){
 library(optmatch)
 match_maker_output<- match_maker(PC = pcs,
                                 eigen_value = eigen_vals,
                                 data = cov_data,
                                 ids = c("sample"),
                                 case_control = c("case"),
                                 num_controls = 1,
                                 eigen_sum = sum(all_eigen_vals),
                                 weight_dist=TRUE
                                 )

# run plot_maker()
 plot_maker(data=match_maker_output,
            x_var="PC1",
            y_var="PC2",
            case_control="case",
            line=TRUE)
 }