Description Usage Arguments Value Note See Also Examples
xSNP2eGenes
is supposed to define eQTL genes given a list of
SNPs or a customised eQTL mapping data. The eQTL weight is calcualted
as Cumulative Distribution Function of negative log-transformed
eQTL-reported signficance level.
1 2 3 4 |
data |
an input vector containing SNPs. SNPs should be provided as dbSNP ID (ie starting with rs). Alternatively, they can be in the format of 'chrN:xxx', where N is either 1-22 or X, xxx is number; for example, 'chr16:28525386' |
include.eQTL |
the eQTL supported currently. By default, it is
'NA' to disable this option. Pre-built eQTL datasets are detailed in
|
eQTL.customised |
a user-input matrix or data frame with 4 columns: 1st column for SNPs/eQTLs, 2nd column for Genes, 3rd for eQTL mapping significance level (p-values or FDR), and 4th for contexts (required even though only one context is input). Alternatively, it can be a file containing these 4 columns. It is designed to allow the user analysing their eQTL data. This customisation (if provided) will populate built-in eQTL data |
cdf.function |
a character specifying a Cumulative Distribution Function (cdf). It can be one of 'exponential' based on exponential cdf, 'empirical' for empirical cdf |
plot |
logical to indicate whether the histogram plot (plus density or CDF plot) should be drawn. By default, it sets to false for no plotting |
verbose |
logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display |
RData.location |
the characters to tell the location of built-in
RData files. See |
a data frame with following columns:
Gene
: eQTL-containing genes
SNP
: eQTLs
Sig
: the eQTL mapping significant level (the
best/minimum)
Weight
: the eQTL weight
none
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## Not run:
# Load the library
library(XGR)
## End(Not run)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
## Not run:
# a) provide the SNPs with the significance info
data(ImmunoBase)
gr <- ImmunoBase$AS$variants
AS <- as.data.frame(GenomicRanges::mcols(gr)[, c('Variant','Pvalue')])
# b) define eQTL genes
df_eGenes <- xSNP2eGenes(data=AS[,1], include.eQTL="JKscience_TS2A",
RData.location=RData.location)
## End(Not run)
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