In yilixu/gwasforest: Make Forest Plot with GWAS Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

gwasforest

The goal of gwasforest is to extract and reform data from GWAS results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).

Installation

The official release version of gwasforest can be installed from CRAN with:

utils::install.packages("gwasforest")

The development version of gwasforest can be installed from GitHub with:

devtools::install_github("yilixu/gwasforest", ref = "main")

Quick Demos

( 1 ) when customFilename (main input) is in dataframe format (with standardized column names):

library(gwasforest)
set.seed(123)

# generate example data
tempValue = runif(n = 18, min = 0.01, max = 2)
tempStdErr = tempValue / rep(3:5, times = 6)
eg_customFilename = data.frame(paste0("Marker", 1:6), tempValue[1:6], tempStdErr[1:6], tempValue[7:12], tempStdErr[7:12], tempValue[13:18], tempStdErr[13:18], stringsAsFactors = FALSE)
colnames(eg_customFilename) = c("MarkerName", paste0(rep("Study", times = 6), rep(1:3, each = 2), rep(c("__Value", "__StdErr"), times = 3)))
rm(tempValue, tempStdErr)

# take a quick look at the example: main input data (with standardized column names)
print(eg_customFilename)

# run gwasforest function
eg_returnList = gwasforest(eg_customFilename, stdColnames = TRUE, valueFormat = "Effect", metaStudy = "Study1", colorMode = "duo")

( 2 ) when customFilename is in dataframe format (without standardized column names), while customFilename_studyName is provided in dataframe format:

# generate example data
tempValue = runif(n = 18, min = 0.01, max = 2)
tempStdErr = tempValue / rep(3:5, times = 6)
eg_customFilename2 = data.frame(paste0("Marker", 1:6), tempValue[1:6], tempStdErr[1:6], tempValue[7:12], tempStdErr[7:12], tempValue[13:18], tempStdErr[13:18], stringsAsFactors = FALSE)
colnames(eg_customFilename2) = c("MarkerName", paste0(rep("Study", times = 6), rep(1:3, each = 2), sample(LETTERS, 6)))
rm(tempValue, tempStdErr)
eg_customFilename_studyName = data.frame("studyName" = paste0("Study", 1:3), stringsAsFactors = FALSE)

# take a quick look at the example: main input data (without standardized column names)
print(eg_customFilename2)

# take a quick look at the example: custom study name
print(eg_customFilename_studyName)

# run gwasforest function
eg_returnList2 = gwasforest(eg_customFilename2, customFilename_studyName = eg_customFilename_studyName, stdColnames = FALSE, customColnames = c("Value", "StdErr"), valueFormat = "Effect", metaStudy = "Study1", colorMode = "duo")

( 3 ) when customFilename_results is in dataframe format (run either of the two steps above to see the example results):

# extract results table
eg_customFilename_results = eg_returnList[[1]]

# take a quick look at the example: results table
print(eg_customFilename_results)

library(ggplot2)

# render plot, see additional NOTES below
plot(eg_returnList[[2]])

( 4 ) NOTES: As shown above, the plot rendered through plot() may suffer from certain issues such as low-resolution and overlapping labels. To overcome these issues and get the genuine plot output from gwasforest, it is recommended to provide a valid outputFolderPath so that a better-rendered plot can be created. The below is the genuine plot output created from the same example data: