metaGE.collect {metaGE} | R Documentation |
Collect the results of GWAS data from different files
Description
This function merges files containing the summary statistics of GWAS in different environments (one file per environment).
Usage
metaGE.collect(
FileNames,
VariableNames,
MinFreq = 0,
DropDuplicates = TRUE,
Verbose = FALSE,
NA.rmv = TRUE
)
Arguments
FileNames |
A list containing the file paths to merge (one trait only) or a list of such lists |
VariableNames |
A named list containing the column names in the original files corresponding to the variables : MARKER, CHR, POS, EFFECT, PVAL (optional: FREQ, ALLELE0, ALLELE1) ; or a list of such lists. |
MinFreq |
A numeric value allowing to filter markers based on the maf. (optional) |
DropDuplicates |
A boolean indicating whether duplicate markers should be removed or not. ( |
Verbose |
A boolean indicating whether progression messages should be printed or not. ( |
NA.rmv |
A boolean indicating if the |
Details
Each file MUST contain the variables below:
MARKER: the marker name
CHR: the chromosome
POS: the position of the marker
EFFECT: the mean effect of the marker
PVAL: the pvalue
Each file might contain the variables:
FREQ: MAF
ALLELE0: Allele coding for allele 0
ALLELE1: Allele coding for allele 1
Value
A list with the following elements:
Data | A tibble containing all the columns of interest of all the files from FileNames. |
RemovedMarkers | Same kind of tibble, but containing the markers that have been removed due to unclear allele coding, maf filtering or duplicates dropping. |
Examples
require(dplyr)
require(tibble)
require(stringr)
RepData <- system.file("extdata", package = "metaGE")
# Get the complete list of association files
File.list <- list.files(RepData ,full.names = TRUE) %>%
tibble(Names = .) %>%
mutate(ShortNames = Names %>%
str_remove(pattern = paste0(RepData,"/")) %>%
str_remove(pattern = "_DF.txt")) %>%
select(ShortNames,Names) %>%
deframe
###Build the dataset
## First provide the list of variable names
Names.list <- list(MARKER="Marker_Name",
CHR="Chromosome",
POS="Marker_Position",
FREQ="Maf",
EFFECT="SNP_Weight",
PVAL="Pvalue",
ALLELE0="Allele1",
ALLELE1="Allele2")
MinFreq <- 0.07
## Now collect
metaData <- metaGE.collect(File.list, Names.list,MinFreq = MinFreq)